L Ramadier

M Zarouk

M Lafourcade

A Micheau

Spreading Relation Annotations in a Lexical Semantic Network Applied to Radiology

Keywords: relation inference, lexical semantic network, relation annotation, radiology

Domain specific ontologies are invaluable but their development faces many challenges. In most cases, domain knowledge bases are built with very limited scope without considering the benefits of including domain knowledge to a general ontology. Furthermore, most existing resources lack metainformation about association strength (weights) and annotations (frequency information like frequent, rare ... or relevance information like pertinent or irrelevant). In this paper, we are presenting a semantic resource for radiology built over an existing general semantic lexical network (JeuxDeMots). This network combines weight and annotations on typed relations between terms and concepts. Some inference mechanisms are applied to the network to improve its quality and coverage. We extend this mechanism to relation annotation. We describe how annotations are handled and how they improve the network by imposing new constraints especially those founded on medical knowledge.

Introduction

For more than two decades, medical practice and bio-medical research have benefited from the availability of biomedical ontologies (Bodenreinder, 2008). These resources are used for semantic analysis such as entity recognition (i.e., the identification of biomedical entities in texts as name of genes, disease, etc.), and relation extraction (i.e., the identification of semantic relationships among biomedical entities like for instance interaction between proteins). In the framework of the UMLS project, which interrelates some 60 controlled vocabularies, an upper-level ontology, the UMLS semantic network [START_REF] Lomax | Mapping the gene ontology into the unified medical language system[END_REF]) has been built. In the field of radiology, such a semantic network is used to facilitate or automate the analysis of radiologist reports in order to extract recommended courses of action or to trigger warning systems to improve patient management [START_REF] Yetisgen-Yildiz | A text processing pipeline to extract recommendations from radiology reports[END_REF]. There exist reference ontologies in biomedical domain (UMLS), but they might not be suited to a particular domain like radiology because result sets are too large and too complex [START_REF] Mejino | FMA-RadLex: An application ontology of radiological anatomy derived from the foundational model of anatomy reference ontology[END_REF]. To solve this problem, the Radiology Society of North America (RSNA) has created reference ontology for radiology RadLex [START_REF] Rubin | Creating and curating a terminology for radiology: ontology modeling and analysis[END_REF]. RadLex and its derivatives rely on English and are not considered medically complete [START_REF] Hong | Analysis of RadLex coverage and term co-occurrence in radiology reporting templates[END_REF].

There is a German version of RadLex (Gertsmair, 2012) but none exist in French, at our knowledge. More importantly, in the domain of radiology, the relationship between terms is crucial and the ontology model might not capture this information as well as a semantic network. The ontology indicates generally only the hierarchy between terms and lacks specific relations relevant either to medicine or how doctors express their knowledge in reports. When making clinical diagnosis based on a radiologist report it is crucial for the medical practitioner to be presented with information from many different non-hierarchical sources but not so important to know the exact hierarchy of a term (as this information is already known beforehand). For example, it is important to give an exhaustive list of symptoms that the medical practitioner should look for regardless of the ontological hierarchy associated with each term. These terms can be better linked when modeled by a semantic network and even better a lexical semantic network taking into account facts of medical language. While general purpose semantic networks will certainly help, they need to be extended to specific domains such as radiology.

The combined method of modeling is important for radiology reports because they contain several distinct sections. In the History section for example, there are typically descriptive texts written in everyday language while in other sections, such as Findings, the language changes to specialized terms. The goal of the construction of this lexical network is to analyze radiological report in order to extract terms and semantic relations between them. Another aim of such research is to carry out a semantic annotation of medical images in order to improve their retrieval.

Lexical-semantic networks can be manually constructed or generated by algorithmic analysis of texts. For instance, the ConceptNet, a freely available general knowledge base, is generated automatically from the 700 000 sentences of the Open Mind Common Sense Project (H [START_REF] Liu | ConceptNet-a practical commonsense reasoning tool-kit[END_REF]. But fully automated generation are generally limited to term co-occurrences as extracting precise semantic relations between terms from corpora remains difficult.

In our combined general purpose-specialized network, we decided to use JeuxDeMots [START_REF] Lafourcade | Making people play for Lexical Acquisition[END_REF]) as a basis for the general purpose network. What we wish to have is a general knowledge base of a very broad scope, in the spirit of Wikipedia but under the machine tractable form of a lexical-semantic network. JeuxDeMots relies on crowdsourcing to manually construct a knowledge base. For this purpose, JeuxDeMots provides a contributive tool called Diko. This tool is important because we can use it to improve the network completeness in specific areas where the game approach is not suitable (relation too complicated, not lexicalized enough). Diko also exploits an inference mechanism (Zarrouk, 2013) to automatically propose relations (between terms) on the basis of what already exists in the network. This approach of inference is strictly endogenous as it does not rely on any external resources. JeuxDeMots uses crowdsourcing to incrementally attribute weight to relations between terms. If a large number of users/players associate two given nodes, the weight will be higher than a link that was only mentioned by fewer users. While this user provided weight is adequate for general purposes, it fails in the diagnostic purpose of radiology reports because the overall frequency of a symptom is not a good indication of its relevance. In a clinical situation, many patients complain of a headache and almost none report arm drift before suffering from a stroke, but arm drift is the most important term. Generally, there is not always a correlation between the associative strength and its importance between two terms. The arc weight indeed implements the associative strength but it correlates neither to the truth nor to the frequency. The medical significance of the relationship should be indicated to generate faithfully this specialist radiology semantic network. The goal of our current work is to develop the cost function that best captures this medical significance and then to train the semantic network through inference mechanisms. We introduce annotation between some relations in the field of radiology in the semantic lexical network. The goal of the relation annotation is to guide the process of inference and semantic analysis.

The rest of the paper is organized as follows. In section 2 we describe the principles behind of lexical network construction and illustrated it with JeuxDeMots. We discuss also about the building of a network specialized in radiology. We present also one type of inference: the deduction scheme. In section 3 we turn to describing the annotation of the relation between medical terms. Section 4 is devoted to describing our experiments and commenting on their results. Section 5 concludes, pointing at avenues for future research.

Lexical Networks

The type of lexical network where are working with is a graph with lexical items or concepts as nodes connected through arcs interpreted as relations between items.

Those relations are semantically typed and represent (typical) lexical or ontological relationship possible between terms (hypernym, synonym, antonym, part of, cause, consequence, typical location, telic role, semantic role and so on). Besides being typed, relations are weighted and directed (no automated symetrisation is undertaken). The contributive approaches for building such a network are more and more popular because they are both cheap to set up and efficient in quality. In recent years, there is an increasing trend of using on-line GWAPs (game with a purpose) [START_REF] Thaler | A survey on games for knowledge acquisition[END_REF] for feeding such resource. The JDM lexical network is constructed through a set of on-line associate games and contributive tools. We briefly describe it in the following section.

The JDM Game Model

JeuxDeMots is a two player blind game based on agreement on term associations. At the beginning of a game session the player is given an instruction related to a target term (for example: give any term that is related to disease). The user has a limited time to enter as many propositions as possible. At the end of the allowed time, player proposals are compared to those of another player for the same game, and points are earned on the basis on the common proposals. Terms in agreement are added to the lexical network with the relation corresponding of the game instruction. If the relation already exists, its weight is increased, otherwise the relation is added. This game is adequate for general common sense knowledge but may be not very efficient for specialized domain. For our project -building a lexical network for radiology, we use a contributive tool, compatible with the JDM lexical network, named Diko that we explain the principle below.

The Contributive Model of Diko and Relation Annotations

Diko is a web based tool for displaying information contained in the JDM lexical network but that can also be used for contribution. The necessity to not be only dependent on the JDM game for the construction of the lexical network comes from the fact that many relation types of JDM are either difficult to grasp for a casual player or not very productive (not possible many answers). In order to build a specialized knowledge we use Diko to propose new relations between terms relevant to the domain at hand. The principle of the contribution process is that a proposition made by a user will be voted pro or con by an expert validator in radiology. In the field of medicine, we added some relations like symptom or diagnostic. This contributive work is needed to build a knowledge substrate for radiology and eventually, the purpose of the project is to extract in a semi-automatic way words and relations from the radiology reports to enhance the specialized network.

To improve the quality of the network, we add more medical significance of relationships between terms thanks to annotations. For instance, for the following relation measles (target) children we can add the annotation "frequent" regardless of the weight of the relation (Fig. 1, and another example is given in Fig. 2). In section 3, we will detail the concept of annotations and their utility. In order to increase the number of relations in the JDM network an inference engine has been proposed. This latter proposes relations as if it was a contributor, to be validated by other human contributors or expert in the case of specialized knowledge. In this paper we describe one type of inference: the deduction scheme.

This deductive scheme is based on the transitivity of the ontological relation is-a (hypernym). If a term A is a kind of B and B has some relation R with C (the premises), then we expect that A holds the same relation with C (the conclusion). The inference engine can be applied on terms having at least one hypernym. If a term has a set of weighted hypernym, the inference engine deduces a set of inferences. These hypernyms are classified according a hierarchical order. The weight of an inference proposed is the incremental geometric mean of each occurrence. In fact, this scheme is too simple, in effect the term B may be polysemous and ways to avoid probably wrong inference can be done by a logical blocking (fig. 3). This mechanism has been described in a previous work (Zarrouk 2013).

Relation Annotations

Generally, above all in specialized knowledge, the correlation between the weight of the relation and its importance is not strict. In the case of hepatocellar carcinoma the relation with wash-out is specific of radiology so the weight of the relation will be low but for the radiologist this relation will be important. This is why it appears interesting to introduce annotations for some relations as they can be of a great help in the medical area. In the lexical network, a relation is represented by a 3-uple:

<Node start , Relation type/annotation, Node end > formally written Node start (Relation type/ annotation) Node end .

For the field of radiology, the most useful relations are shown in table 1. In radiological ontology like RadLex, there are not many relation types or occurence which can be really useful for the analysis of radiological reports. In an information retrieval, this annotation can be helpful to the users. As often, they want to know if a characteristic of one pathology is rare or frequent. This kind of information is generally absent from a network or ontology. For example, the relation between hepatocellular carcinoma and hypervascular are frequent and this information will be directly available in the network. These annotations will have a filter function in the inference scheme. The types of annotations are of several natures (frequency and relevance information). Below, we presented the different main annotation labels.

 frequency annotations : very rare, rare, possible, frequent, always true  usage annotations : o often believed true o language misuse  quantifier : any number, like 1, 2, 4 etc. or many, few  qualitative: pertinent, irrelevant, inferable, potential.

Concerning language misuse, a doctor can use the term flu (illness) instead of virus of influenza: it's a misuse of language as the doctor just makes use of a language shortcut. The annotation often believed true applies for a wrong relation (with a negative weight) which is very often considered as true, for example spider (*isa/often believed true) insect. This kind of annotations could be used to block the inference scheme.

Qualitative annotation relates to the inferable status of a relation, especially concerning inference. The pertinent annotation refers to a proper ontological level for a given relation. For example: living being (carac/pertinent) alive or living being (can/pertinent) die. The inferable annotation is supposedly to be put when a relation is inferable (or has been inferred) from already existing relation, for example : dog (carac/inferable) alive because dog (isa) living being. A potential annotation may be put for terms above pertinent ones in the ontological hierarchy, for examples : bird (haspart/always true) wings and animal (haspart/potential) wings. Finally, the irrelevant annotaion is put for true relation which are considered as too far below the pertinent level, for example animal (haspart/irrelevant) atoms.

The quantifier represents the number of part of a object. Each human has two lungs so quantifier will be 2. This kind of annotation is not necessarily a numeral, but can be of more or less subjective value, like few, many, etc.

The frequency annotations are of five types (always true, frequent, possible, rare and exceptional) and qualitative are two types (pertinent and irrelevant). We have attributed empirical values to each annotation's label like 4 to always true, 3 to frequent, 2 to possible, 1 to rare and 0 to the rest of the annotations. These allow us to select some annotation to facilitate or block the inference scheme.

The first annotations have been made by hand but with the help of inference scheme they will spread through the network. To improve the quality of the network and to prevent some incoherent inferences some kind of annotation will block the potentially absolute relations. For instance, the annotation language misuse or irrelevant will block the inference scheme.

Moreover, to have the most accurate annotation, we need to order the central terms from the most specific to the less specific. That is to say, to reconstitute the taxonomic order related to the hypernym relation (is-a). For the term hepatocellular carcinoma the (several) order of hyponyms will be: hepatocellular carcinoma < malignant tumors of liver < tumor of liver < liver pathology < pathology hepatocellular carcinoma < malignant tumors of liver < tumor of liver < tumor < pathology According to the term the annotation will be different. To choose the right annotation of the new inferred relation, this order plays an important role. The annotation of the most specific term is more crucial (important) than the less specific. We must take into account this fact for the inference mechanism with annotations.

In the inference mechanism, the term B (central term cf. fig. 2) plays a crucial role. We look at the hierarchy of the terms B according to which a specific relation was inferred many times and we keep the most specific. If we end up with two or more terms, we apply the max rules to the values corresponding to each annotation. The result will be the value of the annotation we will give to the inferred relation (Fig 4).

Experimentation

In the previous experiments conducted in (Zarrouk 2013), the deduction engine was applied to the whole lexical network to prove the efficiency of the approach. However, in this paper we unleashed the experiment on a subset of the lexical network JDM which contains all the hypernmy relations (is-a) in which is based the deduction scheme and all the manually annotated relations and that is in order to reduce the search space.

Unleashing relation inference

To increase the result's accuracy and to avoid to infer noisy relations , we blocked inferences on relations which are annotated as irrelevant or exception. Moreover, more detailed results and experiments about the deduction engine are provided in (Zarrouk 2013 (1)). The deduction inference engine applied on 146 934 relations produces a total of 1 825 933 relations with 573 613 distinct ones which make the average of 3 occurrences per relation (Table .2)

Table 2. Number of inferred relations from those already existing ones.

Existing relations 146 934

Inferred relations 1 825 933 Distinct inferred relations 573 613

Spreading relation annotation

The annotations inference engine is the second part of the system. It will be unleashed over the relations (the lexical network) previously enriched with the use of the deduction engine. The relation annotation system runs only on the inferred relations. It takes into consideration the annotations of the premises used to infer a certain relation as mentioned. If there is just one available premise, the annotation of this premise, if any, is affected to the relation inferred. If there are many premises, the system will rebuild the hierarchy between these ones and will keep the annotation of the nearest premise for being the most accurate. In case of having some premises with the same level in the hierarchy, a maximum rule is applied between them and the annotation having the strongest number (always true: 4, frequent: 3, possible:2,.. etc.) will be affected to the inference. This system guarantees a good accuracy of the annotation spreading.

As noticed, contrary to the original deduction engine, we allowed redundancy in because it increases the accuracy of the relation annotation spreading system's results.

To clarify, we propose the following example: The annotation system having these two occurrences (1) and (2) of the same relation stroke (diagnosis) MRI, annotated differently (possible, frequent) will decide to keep the strongest one (frequent). It is informed about the annotation's strength by empirical values we have attributed to each annotation's label according to their frequency like 4 to "always true", 3 to "frequent", 2 to "possible", 1 to "rare" and 0 to the rest of the annotations.

Premises: stroke (is-a) cerebral
The annotation's inference system applied on the relations base stemmed of the deduction engine run, annotated 10 085 relations starting from only 72 ones (Table .3). In this experiment, we have not considered potential and inferable annotations (more than 43 000 distinct annotations for one unique run, 97% are correct and 3% false) because they are more utility annotations than semantically relevant in the context of radiology. Instead, we focused here on the annotations illustrating frequency since it is a very important information in the radiological area.

The number of annotated relations per annotation's label does not depend on the number initially existing as noticed from Table .2, but simply on the number of the ongoing hypernym relations of the central term of the scheme as in the simplified example:

1) The basic inference scheme is the following:

A (is-a) B & B (R/annot) C → A (R/annot) C non-small-cell-lung carcinoma (is-a) malignant tumor hepatocellular carcinoma glioblastoma
& malignant tumor (carac/frequent) poor pronastic →3 relations annotated as frequent (non-small-cell-lung carcinoma /hepatocellular carcinoma/ glioblastoma (carac/frequent) poor pronastic)

The larger the number of hypernym relations toward the term B (malignant tumor) which has an outgoing relation annotated (malignant tumor (carac/frequent) poor pronastic), the larger is the number of annotated relations.

2) However, for the existing annotated relations which do not contribute a lot in the inferring process, as the annotation frequent (Table .2), they are attributed to relations which are ineligible to the annotations scheme which is a deductive like for example: Hepatocellular carcinoma (carac/frequent) hypervascular

The term Hepatocellular carcinoma does not have any ongoing hypernym relation (x (is-a) Hepatocellular carcinoma), so in this case the annotation frequent will not generate other annotations.

We statistically evaluated the produced annotation, and it appears than 87% of them have been evaluated as "correct", 5 % as "incorrect" and the rest (8 %) as "debatable" (that is to say that experts might discuss not its validity but rather if the frequency value should be modify). The evaluation has been done manually by three experts on random sample of at least 100 annotations up to 10% for each annotation values. Each evaluator had to choose between the three possible values above: correct, incorrect and debatable. The Cohen's kappa coefficient was equal to 0.83.

A debatable result is one felt by the evaluator as not incorrect but where points of view can be in conflict. Most of the cases are between frequent and possible, or to a lesser extend between rare and very rare.

In this experiment we applied the relation/annotation system through a single run. But naturally, the system which is actually running iteratively along the contributions and the games uses the new terms and annotations added and the previously inferred ones to continue annotating more relations.

Conclusion

Annotations viewed as information added to typed relations between terms add a new dimension in the knowledge contained in lexical networks. Even when weighted, relation strength not always relates to its confidence. Something can be rare but very important, and conversely a relation can be so obvious that its intensity is low.

We presented some issues related to building a lexical semantic network with games and user contributions and about inferring new annotated relations from existing ones. To be able to enhance the network quality and coverage, we proposed a consolidation approach based on a relations and annotations inference engine. The annotation system we presented in this paper is a complement for the lexical network consolidation system presented in (Zarrouk, 2013 (2)). This enhanced consolidation approach can provide, thanks to the annotation system, a crucial information which can be used not only in radiology as shown previously but also in other specialized domains, and certainly for common sense reasoning.

It seems to us interesting to develop knowledge in a specialized domain inside a general lexical network. Further research must improve the spreading relation annotation and also the specialized lexical in radiology with the help of expert but also non expert contributors.

Fig. 1 .

 1 Fig. 1. Example of term "measles" with annotations between brackets. Several annotations are possible for a given relation like frequent.

Fig. 2 .

 2 Fig. 2. Example of term "multiple sclerosis" which has for example as causes genetic factors and environmental factors annotated possible but uncertain.

Fig. 3 .

 3 Fig. 3. Deductive inference scheme with logical blocking. If A is a B and B has some relation R with C, then it is expected that A has the same relation R with C. However, if B is polysemous, and two different refinement (B' and B'') hold the premises, then the relation A R C is most probably wrong.

Fig. 4 .

 4 Fig. 4. Approach based on hierarchy used to choose the most accurate annotation to give to an inferred relation via several central terms

Table 1 .

 1 Relevant relations in the radiology field with explanation, examples and their annotations

Relation type Explanation, examples and annotation is

  has-parts Element of the term, liver has part segment I (always true) characteristic Hepatocellular carcinoma carac hypervascular (frequent) typical location Typical place where can be the term/object in question, multiple sclerosis typ location central nervous system (always true) target Population affected by the term, measles target children (frequent) diagnosis Examen, multiple sclerosis diag MRI (frequent, crucial)

	symptom Symptom, measles symptom fever (frequent)
	against What the start term opposes/fight/prevents, malignant tumor
	against chemotherapy (frequent)
	cause B(that you have to give) is a cause of A, cirrhosis cause alcoholism
	(frequent)
	consequence The end term is a possible consequence of the start term, stroke
	consequence hémiplegia (possible)

-a Hypernym, MRI is-a medical imaging

(possible)

Table 3 .

 3 Number of annotations inferred after the application of the relation annotation system on the existing ones

	Annotation's Label	Existing annotation	Inferred annotation
	Frequency: frequent &always true	38	8 093
	Frequency: possible	16	150
	Frequency: rare & very rare	7	35
	Qualifier: often believed true	1	7
	Qualifier: irrelevant	5	1 604
	Quantifier	5	178
	Total	72	10 085

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Lionel Ramadier
email: lionel.ramadier@lirmm.fr

Manel Zarrouk
email: manel.zarrouk@lirmm.fr

Mathieu Lafourcade
email: mathieu.lafourcade@lirmm.fr

Antoine Micheau
email: antoine.micheau@imaios.com

Inferring Relations and Annotations in Semantic Networks Application to Radiology

Keywords: relation inference, lexical semantic network, relation annotation, radiology

Domain specific ontologies are invaluable despite many challenges associated with their development. In most cases, domain knowledge bases are built with very limited scope without considering the benefits of plunging domain knowledge to a general ontology. Furthermore, most existing resources lack meta-information about association strength (weights) and annotations (frequency information like frequent, rare ... or relevance information like pertinent or irrelevant). In this paper, we are presenting a semantic resource for radiology built over an existing general semantic lexical network (JeuxDeMots). This network combines weight and annotations on typed relations between terms and concepts. Some inference mechanisms are applied to the network to improve its quality and coverage. We extend this mechanism to relation annotation. We describe how annotations are handled and how they improve the network by imposing new constraints especially those founded on medical knowledge.

Introduction

For more than two decades medical practice and biomedical research have benefited from the availability of biomedical ontologies [START_REF] Bodenreider | Biomedical ontologies in action: role in knowledge management, data integration and decision support[END_REF]. These resources are used for semantic analysis such as entity recognition (i.e., the identification of biomedical entities in texts as name of genes, disease, etc.), and relation extraction (i.e., the identification of semantic relationships among biomedical entities like for instance interaction between proteins). In the framework of the UMLS project, which interrelates some 60 controlled vocabularies, upper-level ontology, the UMLS semantic network [START_REF] Lomax | Mapping the gene ontology into the unified medical language system[END_REF] has been built. In the field of radiology, such a semantic network is used to facilitate or automate the analysis of radiologist reports in order to extract recommended courses of action or to trigger warning systems to improve patient management [START_REF] Yetisgen-Yildiz | A text processing pipeline to extract recommendations from radiology reports[END_REF]. There exist reference ontologies in biomedical domain (UMLS), but they might not be suited to a particular domain like radiology because result sets are too large, too complex [START_REF] Mejino | FMA-RadLex: An application ontology of radiological anatomy derived from the foundational model of anatomy reference ontology[END_REF]. To solve this problem, the Radiology Society of North America (RSNA) has created reference ontology for radiology RadLex [START_REF] Rubin | Creating and curating a terminology for radiology: ontology modeling and analysis[END_REF]. RadLex and its derivatives rely on English and are not considered medically complete [START_REF] Hong | Analysis of RadLex coverage and term cooccurrence in radiology reporting templates[END_REF]. There is a German version of RadLex [START_REF] Gerstmair | Intelligent image retrieval based on radiology reports[END_REF] but none exist in French, at our knowledge. More importantly, in the domain of radiology, the relationship between terms is crucial and the ontology model might not capture Computación y Sistemas Vol. XX No. X, 20XX pp XX-XX ISSN 1405-5546 this information as well as a semantic network. The ontology indicates generally only the hierarchy between terms and lacks specific relations relevant either to medicine or how doctor express their knowledge in reports. When making clinical diagnosis based on a radiologist report it is crucial for the medical practitioner to be presented with information from many different non-hierarchical sources but not so important to know the exact hierarchy of a term (as this information is already known beforehand). For example, it is important to give an exhaustive list of symptoms or characteristic of a disease that the medical practitioner should look for regardless of the taxonomic hierarchy associated with each term. These terms can be better linked when modeled by a semantic network and even better a lexical semantic network taking into account facts of medical language. While general purpose semantic networks will certainly help, they need to be extended to specific domains such as radiology.

The combined method of modeling is important for radiology reports because they contain several distinct sections. In the History section for example, there are typically descriptive texts written in everyday language while in other sections, such as Findings, the language changes to specialized terms. The goal of the construction of this lexical network is to analyze radiological report in order to extract terms and their relations (between them). An aim is to carry out a semantic annotation of medical images to improve their retrieval.

Lexical-semantic networks can be manually constructed or generated by algorithmic analysis of texts. For instance, the ConceptNet, a freely available general knowledge base, is generated automatically from the 700 000 sentences of the Open Mind Common Sense Project [START_REF] Liu | ConceptNet-a practical commonsense reasoning tool-kit[END_REF]. But fully automated generation are generally limited to term cooccurrences as extracting precise semantic relations between terms from corpora remains difficult.

In our combined general purpose-specialist network, we decided to use JeuxDeMots [START_REF] Lafourcade | Making people play for Lexical Acquisition[END_REF] as a basis for the general purpose network. What we wish to have is a general knowledge base. JeuxDeMots relies on crowdsourcing to manually construct a knowledge base. For this purpose, JeuxDeMots provides a contributive tool called Diko. This tool is important because we can use it to improve the network completeness in specific areas where the game approach is not suitable (relation too complicated, not lexicalized enough). Diko also exploits an inference mechanism [START_REF] Zarrouk | Inference and Reconciliation in a Crowdsourced Lexical-Semantic Network[END_REF] to automatically propose relations (between terms) on the basis of what already exists in the network. This approach of inference is strictly endogenous as it does not rely on any external resources. JeuxDeMots uses crowdsourcing to incrementally attribute weight to relations between terms. If a large number of users/players associate two given nodes, the weight will be higher than a link that was only mentioned by fewer users. While this user provided weight is adequate for general purposes, it fails in the diagnostic purpose of radiology reports because the overall frequency of a symptom is not a good indication of its relevance. In a clinical situation, many patients complain of a headache and almost none report arm drift before suffering from a stroke, but arm drift is the most important term. Generally, there is not always a correlation between the associative strength and its importance between two terms. The arc weight indeed implements the associative strength but it correlates neither to the truth nor to the frequency. The medical significance of the relationship should be indicated to generate faithfully this specialist radiology semantic network. The goal of our current work is to develop the cost function that best captures this medical significance and then to train the semantic network through inference mechanisms. We introduce annotation between some relations in the field of radiology in the semantic lexical network. The goal of the relation annotation is to guide the process of inference and semantic analysis.

The rest of the paper is organized as follows. In section 2 we describe the principles behind of lexical network construction and illustrated it with JeuxDeMots. We discuss also about the building of a network specialized in radiology. We present also one type of inference: the deduction scheme. In section 3 we turn to describing the annotation of the relation between medical terms. Section 4 is devoted to describing our experiments and commenting on their

Lexical Networks

The type of lexical network where are working with is a graph with lexical items or concepts as nodes connected through arcs interpreted as relations between items. Those relations are semantically typed and represent (typical) lexical or ontological relationship possible between terms (hypernym, synonym, antonym, part of, cause, consequence, typical location, telic role, semantic role, characteristic and so on). Besides being typed, relations are weighted and directed (no automated symetrisation is undertaken). The contributive approaches for building such a network are more and more popular because they are both cheap to set up and efficient in quality. In recent years, there is an increasing trend of using on-line GWAPs (game with a purpose) [START_REF] Thaler | A survey on games for knowledge acquisition[END_REF] for feeding such resource. The JDM lexical network is constructed through a set of on-line associate games and contributive tools. It exists 10 173 073 relations and 368 604 terms in JDM. We use this GWAP (and also a contributive tool) to build our domain specific ontology. For the word medicine there is around 11 000 relations in the database. In the network around 350 relations exist for the word MRI (fig 1 in French). We briefly describe it in the following section.

The JDM Game Model

JeuxDeMots is a two player blind game based on agreement on term associations. At the beginning of a game session the player is given an instruction related to a target term (for example: give any term that is related to disease). The user has a limited time to enter as many propositions as possible. At the end of the allowed time, player proposals are compared to those of another player for the same game, and points are earned on the basis on the common proposals.

Terms in agreement are added to the lexical network with the relation corresponding of the game instruction. If the relation already exists, its weight is increased, otherwise the relation is added. This game is adequate for general common sense knowledge but may be not very efficient for specialized domain. For our project -building a lexical network for radiology, we use a contributive tool, compatible with the JDM lexical network, named Diko that we explain the principle below.

The Contributive Model of Diko and Relation Annotations

Diko is a web based tool for displaying information contained in the JDM lexical network but that can also be used for contribution. The necessity to not be only dependent on the JDM game for the construction of the lexical network comes from the fact that many relation types of JDM are either difficult to grasp for a casual player or not very productive (not possible many answers). In order to build a specialized knowledge we use Diko to propose new relations between terms relevant to the domain at hand. The principle of the contribution process is that a proposition made by a user will be voted pro or con by an expert validator in radiology. In the field of medicine, we added some relations like symptom or diagnostic. But most generic concepts were able to be connected with the radiological concepts (relation location, has part, and so on). This contributive work is needed to build a knowledge substrate for radiology and eventually, the purpose of the project is to extract To improve the quality of the network, we add more medical significance of relationships between terms with the help of annotations. For instance, for the following relation meningioma (is-a) benign central nervous system neoplasm we can add the annotation frequent regardless of the weight of the relation (Fig.

2.

). Another example is given in figure 3. In section 3, we present in details the concept of annotation and its utility. In order to increase the number of relations in the JDM network an inference engine has been proposed. This latter propose relations as if it was a contributor, to be validated by human contributors or expert in the case of specialized knowledge. In this paper we describe one type of inference: the deduction scheme.

This deductive scheme is based on the transitivity of the ontological relation is-a (hypernym). If a term A is a kind of B and B has some relation R with C (the premises), then we expect that A holds the same relation with C (the conclusion). The inference engine can be applied on terms having at least one hypernym. If a term has a set of weighted hypernym, the inference engine deduces a set of inferences. This hypernym are classified according a hierarchical order. The weight of an inference proposed is the incremental geometric mean of each occurrence. In fact, this scheme is too simple, in effect the term B may be polysemous and ways to avoid probably wrong inference can be done by a logical blocking (fig. 4). This mechanism has been described in a previous work [START_REF] Zarrouk | Inference and Reconciliation in a Crowdsourced Lexical-Semantic Network[END_REF]. In case of invalidation of an inferred relation, a reconciliator is invoked to try to assess why the inferred relation is wrong. The reconciliation allows us to identify the cause of the wrong inference: an exception, an error in the premises or transitivity confusion due to polysemy with the identification of the proper word senses at stake. In what follows, it is this kind of inference that we are going to consider (about annotation). Nevertheless, there are two other types of inference: the induction (from specific to general) and abduction (imitation from examples). However, annotation propagation for this type of inference is quite hazardous.

Relation Annotations

In all generality, above all in specialized knowledge, the correlation between the weight of the relation and its importance is not strict. In the case of hepatocellar carcinoma the relation with wash-out is specific of radiology so the weight of the relation will be low but for the radiologist this relation will be important. This is why it appears interesting to introduce annotations for some relations as they can be of a great help in the medical area.

Relation Types for Radiology

In the lexical network, a relation is formally represented by a 3-uple: <Node start , Relation type/annotation, Node end > formally written Node start (Relation type/ annotation) Node end .

Concerning the field of radiology, the most useful relations which were established by radiologist following their daily clinical practice are shown in table1. In radiological ontology like RadLex, there is not so much relation which can useful in the analysis of radiological reports. In an information retrieval, this annotation can helpful to the users. As often, they want to know if a characteristic of a pathology is rare or frequent. This kind of information is generally absent from a network or ontology. For example, the relation between measles and children are frequent and this information will be directly available in the network (fig 5).

Annotations Values

These annotations will have a filter function in the inference scheme. The types of annotations are of various natures (mostyl frequency and relevance information). We presents below the different main annotation labels.

 frequency annotations : very rare, rare, possible, frequent, always true  usage annotations : often believed true, language misuse  quantifier : any number, like 1, 2, 4 etc. or many, few  qualitative: pertinent, irrelevant, inferable, potential, prefered.

Concerning language misuse, a doctor can use the term flu (illness) instead of virus of influenza: it's a misuse of language as the doctor just makes use of a language shortcut. The annotation often believed true applies for a wrong relation (with a negative weight) which is very often considered as true, for example spider (*is-a/often believed true) insect. This kind of annotations could be used to block the inference scheme.

Qualitative annotation relates to the inferable status of a relation, especially concerning inference. The pertinent annotation refers to a proper ontological level for a given relation. For example: living being (carac/pertinent) alive or living being (can/pertinent) die. Another example is about the synonym where, under the advice of expert, it is relevant to choose a favorite synonym. For instance for the following synonym: hepatocellular carcinoma (favorite), HCC, malignant hepatoma.

The inferable annotation is supposedly to be put when a relation is inferable (or has been inferred) from already existing relation, for example: dog (carac/inferable) alive because dog (isa) living being.

A potential annotation may be put for terms above pertinent ones in the ontological hierarchy, for examples: bird (haspart/always true) wings and animal (haspart/potential) wings. Finally, the irrelevant annotation is put for true relation which is considered as too far below the pertinent level, for example animal (haspart/irrelevant) atoms.

The quantifier represents the number of part of a object. Each human has two lungs so quantifier will be 2. This kind of annotation is not necessarily a numeral, but can be of more or less subjective value, like few, many, etc.

The frequency annotations are of five types (always true, frequent, possible, rare and exceptional) and qualitative are two types (pertinent and irrelevant).

We annotation's label like 4 to always true, 3 to frequent, 2 to possible, 1 to rare and 0 to the rest of the annotations. These allow us to select some annotation to facilitate or block the inference scheme. The first annotations have been made by hand but with the help of inference scheme they will spread through the network. To improve the quality of the network and to prevent some incoherent inferences some kind of annotation will block the potentially absolute relations. For instance, the annotation language misuse or irrelevant will block the inference scheme.

The possible annotation, is a special case. Depending of the configuration of the system, it may block (stricter approach) of not block (lenient approach) the inference mechanisme. If the system is lenient, we may obtain many inference proposals that might be wrong (high recall, low precision). On the other hand, if the system is strict, we reduce the risk of wrong proposals, but at the cost of missing adequate ones (low recall, high precision).

Using Generic Partial Orders

Moreover, to have the most accurate annotation, we need to order the central terms from the most specific to the less specific as we wrote down in [START_REF] Ramadier | Spreading Relation Annotations in a lexical semantic network: Applied to radiology: In CICLING[END_REF]. That is to say, we try to reconstitute the taxonomic order related to the hypernym relation (is-a). Here we explain in detail the algorithm. The Generic Paths Algorithm has been designed to this purpose.

For example, for the term hepatocellular carcinoma the (several) partial orders for its hypernyms will be: The annotation will be different, depending of the term at hand. To choose a proper annotation (or several) for the new inferred relation, this order plays an important role. The annotation, if any, of the most specific term is more crucial than less general terms (at higher levels og the hierarchy).

 hepatocellular carcinoma →
We must take into account this fact and exploit it when dealing with the inference mechanism with annotations.

The idea of the Generic Paths Algorithm is to produce ordered lists of generic from a given term. The lists are ordered from the most specific (in fact, the target term) to the most general. When produced, these lists are useful for various kinds of propagation algorithms, amongst which the one we are using for propagating annotations.

First, an unordered list of generic is extracted from the lexical network. This list has first to be filtred in order to keep only one term in case of writting variants and strict synonyms. On the example above, tumeur du cerveau and tumeur cérébrale are strict synonyms, and only one of this is kept as representative for producing paths. We note this list of filtered terms as FT.

The second step is order each term as if they all follow an order relation. Note the produced result as a set of paths P = {p 1 ... p n } where p are paths. A path is an ordered set of terms which belongs to FT. We initialize P to the empty set.

For each t ∈ FT, we insert it at its proper place in each p of P (like any sorting algorithm by insertion). If t cannot be included, we add a new list to P (P = P ∪ {t}). In a given path p, a term t can be inserted between two consecutive terms ta and tb if and only if (test function) ta < t and t <tb, where x < y means that y is a generic of x.

When all terms t have been added to one or several path of P, the algorithm ends and the result is P. Again, P is not the difinition of the hierarchy graph, but a set of paths (ordered sequence of term) thatcompletly cover the graph (no term are left out).

The process has a cubic complexity in the number of terms, considering the test function as basic operation. This polynomial complexity is definitively manageable in practice as the actual number of generic terms is rarely over 100. Hence, the hierarchy paths can be computed on the fly, and barely need to be stored in the database.

Even for monosemic terms, most of the time, multiple views related to the term lead to a hierarchy which takes the form of a direct acyclic graph, and always not a linear chain of generic terms. The term méningione, which is monosemic is a typical example.

Fig. 7a andb. Use of the hierarchy used to select the most accurate annotation to link to an inferred relation via several central terms. The most specific terms are selected, and the max rule applied.

In the inference mechanism, the term B (central term cf. fig. 4) plays a crucial role. We look at the hierarchy of the terms B according to which a specific relation was inferred many times and we keep the most specific. If we end up with two or more terms, we apply the max rules to the values corresponding to each annotation. The result will be the value of the annotation we will give to the inferred relation (Fig 7a and7b).

Computación y Sistemas Vol. XX No. X, 20XX pp XX-XX ISSN 1405-5546

Experimentation

In some previous experiments [START_REF] Zarrouk | Inference and Reconciliation in a Crowdsourced Lexical-Semantic Network[END_REF], the deduction engine was applied to the whole lexical network in order to assess the approach efficiency. In this paper we unleashed the experiment on a subset of the lexical network JDM which contains all the hypernmy relations (generic / is-a) in which is based the deduction scheme and all the manually annotated relations. This reduction allowed to reduce drastically the size of the search space.

Unleashing Relation Inference

To increase the result's accuracy and to avoid to infer noisy relations, we blocked inferences on relations which are annotated as irrelevant or exception. Moreover, more detailed results and experiments about the deduction engine are provided in [START_REF] Zarrouk | Inference and Reconciliation in a Crowdsourced Lexical-Semantic Network[END_REF]. The deduction inference engine applied on around 150 000 relations produces over 2 millions relations with more than 700 000 distinct ones which makes an average of 3 occurrences per relation (cf.

Relation Annotation Propagation

The annotations inference engine is applied as the second part of the system. So it will be unleashed over the relations base previously enriched with the deduction engine. The relation annotation system run only on the inferred relations, it takes in consideration the annotations of the premises used to infer a certain relation as mentioned. If there is just one premise, the annotation of this premise, if any, is affected to the relation inferred. If there are many premises, the system will rebuild the hierarchy between these ones and will keep the annotation of the nearest premise for being the most accurate. In case of having some premises with the same level in the hierarchy, a maximum rule is applied between them and the annotation having the strongest number (always true: 4, frequent: 3, possible:2, etc.) will be affected to the inference. This system guarantees a good accuracy of the annotation spreading.

As noticed, contrary to the original deduction engine, we allowed redundancy in because it increases the accuracy of the relation annotation spreading system's results.

To clarify we propose the following example:

Premises: stroke (is-a) cerebral
The annotation system having these two occurrences (1) and (2) of the same relation stroke (diagnosis) MRI, annotated differently (possible, frequent) will decide to keep the strongest one (frequent). It is informed about the annotation's strength by empirical values we have attributed to each annotation's label according to their frequency like 4 to "always true", 3 to "frequent", 2 to "possible", 1 to "rare" and 0 to the rest of the annotations.

The annotation's inference system applied on the relations base stemmed from the deduction engine run, annotated more than 10 000 relations starting from only one hundred ones (Table .3). The ratio between existing relations and produced ones at this stage is over 1 for 100.

In this experiment, we have not considered potential and inferable annotations (more than 43 000 distinct annotations for one unique run, 97% are correct and 3% false) because they are more utility annotations than semantically relevant in the context of radiology. Instead, we focused here on the annotations illustrating frequency since it is very important information in the radiological area. The number of annotated relations per annotation's label does not depend on the number initially existing as noticed from Table .2, but simply on the number of the ongoing hypernym relations of the central term of the scheme as in the simplified example:

The basic inference scheme is the following:

A (is-a) B & B (R/annot) C → A (R/annot) C

For example:

non-small-cell-lung carcinoma (is-a) malignant tumor hepatocellular carcinoma glioblastoma & malignant tumor (carac/frequent) poor pronastic →3 relations annotated as frequent (non-small-celllung carcinoma /hepatocellular carcinoma/ glioblastoma (carac/frequent) poor pronastic)

The larger the number of hypernym relations toward the term B (malignant tumor) which has an outgoing relation annotated (malignant tumor (carac/frequent) poor pronastic), the larger is the number of annotated relations.

However, for the existing annotated relations which do not contribute a lot in the inferring process, as the annotation frequent (Table 2), they are attributed to relations which are ineligible to the annotations scheme which is a deductive like for example:  Hepatocellular carcinoma (carac/frequent) hypervascular

The term Hepatocellular carcinoma does not have any ongoing hypernym relation (x (is-a) hepatocellular carcinoma), so in this case the annotation frequent will not generate other annotations.

We statistically evaluated the produced annotation, and it appears than 87% of them have been evaluated as "correct", 5 % as "incorrect" and the rest (8 %) as "debatable" (that is to say that experts might discuss not its validity but rather if the frequency value should be modify).

In this experiment we applied the relation/annotation system through a single run. But naturally, the system which is actually running iteratively along the contributions and the games uses the new terms and annotations added and the previously inferred ones to continue annotating more relations.

Conclusion

In this article, we presented some issues concerning relation annotation in the context of 1) building a lexical semantic network with games and user contributions and 2) inferring new relations from existing ones. To be able to enhance the network quality and coverage, we proposed a consolidation approach based on a relations inference engine taking into account relation annotations. The annotation system we presented in this paper is complementary with the lexical network consolidation system presented in [START_REF] Zarrouk | Inference and Reconciliation in a Crowdsourced Lexical-Semantic Network[END_REF]. This enhanced lexical network consolidation approach can provide (with the help to the annotation system) some important information that can be used for analyzing reports not only in Computación y Sistemas Vol. XX No. X, 20XX pp XX-XX ISSN 1405-5546 radiology domain as shown previously, but also in other specific domains and certainly for application needing common sense reasoning.

It seems to us interesting and valuable to develop knowledge representation for a specialized domain like radiology or parasitology as being included into a general lexical network. Indeed, specialized knowledge comes along common sense (but obviously not always the other way around). To automatically understand medical reports of a given specially, common sense inference is at least as important as specific knowledge of the domain.

Further research must improve the spreading relation annotation and also the specialized lexical inference (in radiology but also any other domain of interest) with the help of both experts and non expert contributors.

The annotation schema as presented here is a first step toward representation of beliefs, uncertain knowledge and points of view. Being able to make inferences both about facts and annotations in such a context in one of the major next step toward automatic semantic analysis exhibiting some intelligence.

  Computación y Sistemas Vol. 15 No. 2, 2011 pp ISSN 1405-5546 results. Section 5 concludes, pointing at avenues for future research.

Fig. 1 .

 1 Fig.1. Example of network for the word MRI

  Computación y Sistemas Vol. XX No. X, 20XX pp XX-XX ISSN 1405-5546 in a semi-automatic way words and relations from the radiology reports to enhance the specialized network. The building of imaging medical network was realized from 40 000 radiology reports representing the different medical imaging techniques (MRI, CTscan, ultrasound and so on). The first step consisted in accomplishing an inverted index of bigram, trigram and quadrigram. In the second step the expert submitted to the other domain specialist the term or concept that he considered relevant for a process of validation/invalidation.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Example of term "meningioma" with annotations between brackets. Several annotations are possible for a given relation like frequent

Fig. 4 .

 4 Fig. 4. Figure reproduced from [12]. Informal deductive inference scheme with logical blocking. If A is a B and B has some relation R with C, then it is expected that A has the same relation R with C. However, if B is polysemous, and two different refinement (B' and B'') hold the premises, then the relation A R C is most probably wrong.

Fig. 5 .

 5 Fig.5. Relation annotation implementation. A given relation to be annotated is reified (represented by a specific node, here some red circles) and this node is associated to various annotations and any other regular terms. The annotation relation type is a kind of relation among others.

Fig. 6 .

 6 Fig.6. The hierarchy implicitly described for the term méningiome. Each term of the hierarchy is included in at least one the the path (path lists presented above). The hierarchy can be partially deduced from the paths.

  Computación y Sistemas Vol. 15 No. 2, 2011 pp ISSN 1405-5546 In the example above (méningiome), the raw list of generic terms (RT) is the following: tumeur • maladie>médecine • tumeur bénigne • maladie neurologique • tumeur bénigne du cerveau • tumeur du cerveau • tumeur cérébrale • tumeur intracrânienne • tumeur bénigne du système nerveux central • tumeur bénigne de la moelle épinière • tumeur du système nerveux central • tumeur des méninges • tumeur maligne du système nerveux central • tumeur maligne • tumeur de la moelle épinière.

Table 1 .

 1 Relevant relations in the radiology's field with explanation, examples and their annotations

	Relation type	Explanation, examples and typical annotations
	is-a	Hypernym, MRI is-a medical imaging (possible)
	has-parts	Element of the term, liver has part segment I (always true)
	characteristic	Hepatocellular carcinoma carac hypervascular (frequent)
	typical location	Typical place where can be the term/object in question, multiple sclerosis

typ location central nervous system (always true) Computación y Sistemas Vol. XX No. X, 20XX pp XX-XX ISSN 1405-5546

Table 1 .

 1 Table.2) Number of inferred relations from those already existing ones.

	Existing relations	153 765
	Inferred relations	2 123 533
	Distinct inferred relations	729 510

Table 2 .

 2 Number of annotations inferred after the application of the relation annotation system on the existing ones

	Annotation's Label	Existing annotation	Inferred annotation
	Frequency: frequent &always true	38	8 709
	Frequency: possible	16	172
	Frequency: rare & very rare	7	42
	Qualifier: often believed true	1	9
	Qualifier: irrelevant	27	2 387
	Quantifier	12	178
	Total	103	11 497

Computación y Sistemas Vol. 15 No. 2, 2011 pp ISSN 1405-5546


Mathieu Lafourcade
email: mathieu.lafourcade@lirmm.fr

Medical Imaging Report Indexing: Enrichment of Index through an Algorithm of Spreading over a Lexico-semantic Network

In medical imaging domain, digitized data is rapidly expanding Therefore it is of major interest for radiologists to be able to do an efficient and accurate extraction of imaging and clinical data (radiology reports) which are essential for a rigorous diagnosis and for a better management of patients. In daily practice, radiology reports are written using a nonstandardized language which is often ambiguous and noisy. The queries of radiological images can be greatly facilitated through textual indexing of associated reports. In order to improve the quality of the analysis of such reports, it is desirable to specify an index enlargement algorithm based on spreading activations over a general lexical-semantic network. In this paper, we present such an algorithm along with its qualitative evaluation.

Introduction

Widespread digitalization in the health care sector and the implementation of customized electronic medical record result in a rapid increase in the volume of digital medical data. The medical computer systems allow to archive many and varied information (for example medical record, results of medical analyses, X-rays and radiological reports…). Thus, these data are accessible, either to be completed and compared with new results or to adapt the management of patients, or to provide decision support to improve the quality of care. The ability to have easily and efficiently access to these medical data has become a primary objective for health professionals. Thus, a proper indexing of medical reports (surgical, radiological...) optimizes the search for information, not only in a clinical purpose, but also educational. [START_REF] Dinh | Vers un modèle d'indexation sémantique adapté aux dossiers médicaux de patients[END_REF] then realized a semantic indexing of patients' medical records in order to make it support for some information search procedures. Their indexing method uses MeSH (Medical Subject Headings), involves disambiguation, the extraction of clinical values, and weighting of concepts. [START_REF] Pouliquen | Indexation de textes médicaux par extraction de concepts, et ses utilisations[END_REF] also performed an automatic indexation through recognition and extraction of medical concepts. He took into account the compound words and word associations to convert a sentence in reference words with the help of a medical thesaurus.

In the field of the medical imaging, the quantity of images and reports increases so much that being able to find quickly and easily the information becomes a major stake. But take full advantage of such a collection of radiological images means being able to quickly identify relevant information and requires that they are properly indexed from their reports. To be effective and useful for practitioners, indexing must consider their requests. Several authors, including [START_REF] Mailhot | Selective automated indexing of findings and diagnoses in radiology reports[END_REF] and [START_REF] Huang | A pilot study of contextual UMLS indexing to improve the precision of concept-based representation in xmlstructured clinical radiology reports[END_REF] automatically indexed radiological reports using the UMLS metathesaurus. To improve the accuracy of their results, they used a subsection of UMLS terminology, and Hersh et al., (2001) deliberately choose not to include some parts of reports, especially the indications section. They thus obtained an index limited to strictly medical terms. However, in practice, in order to efficiently search, practitioners must have the possibility to specify in their requests not only medical specific terms (digestive perforing, glioblastoma), but also expressions, compound words, and circumlocutions of general sense (skiing accident, breast disease in young women, hangman fracture, trauma of the lower limbs).

Automatic extraction of relevant information from medical corpus is complex for several reasons: firstly, most texts are not structured and contain abbreviations, ellipsis and inaccuracies, on the other hand the amount of information to be analyzed is large and relevance is difficult to determine. The obstacles that hinder relevant indexing are of all kinds: difficulty of automatic semantic analysis (especially the precise analysis of negations, as shown by [START_REF] Huang | A novel hybrid approach to automated negation detection in clinical radiology reports[END_REF]), of identifying apocopes (flu for influenza) or unfamiliar terms (i.e. absent from the knowledge base), of recognition of medical entities often present in a distorted writing style, of extraction of semantic relations present in the text [START_REF] Bundschus | Extraction of semantic biomedical relations from text using conditional random fields[END_REF], etc. To carry out a good indexing, it is crucial to have a knowledge base not only broad-spectrum (i.e. not limited to standardized forms) but also dynamic (i.e. able to evolve and enrich itself by permanent learning).

As far as we know, until now, the automatic indexing of radiological reports has concerned mainly medical terms without considering the general information. However, [START_REF] Xu | Anatomical entity recognition with a hierarchical framework augmented by external resources[END_REF] were able to identify named entities of anatomical terms using some general resources like Wikipedia and Wordnet besides the usual medical resources i.e. UMLS, RadLex, MeSH et BodyPart3D (http://lifesciencedb.jp/bp3d/). Another type of resource, which had never been used in the medical or biomedical framework allows to consider not only the words and concepts of specialty, but also the common language used in reports (including the indications section). This is the lexical-semantic JeuxDeMots network (http://www.jeuxdemots.org) we use as a basis of knowledge and support for automatic indexing of radiological reports.

One objective of IMAIOS project (that we are conducting in collaboration with radiologists from Montpellier) is to achieve efficient indexing of radiological reports. To this end, we do not use only a description of the terms and concepts of specialty, but we are also working to determine the meaning and usage of terms and abbreviations very common in medicine. [START_REF] Mcinnes | Determining the difficulty of word sense disambiguation[END_REF] stressed the difficulty of indexing in the biomedical field, and [START_REF] Ramadier | Annotations et inférences de relations dans un réseau lexico-sémantique : application à la radiologie[END_REF] tries to make the task easier by using annotations and inferences from semantic relations. In this article we show how one can, from the semantic information of reports (in French), set an enlargement of raw built index to improve the recall of information retrieval. Indeed, radiologists may express their queries using generic terms (e.g. benign brain tumor, brain tumor, benign tumor, tumor) or consequences, or circumstances, etc. without these terms or expressions are explicitly present in reports. This semantic indexation may be also combined with the content-based image retrieval (CBIR) [START_REF] Kurtz | A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations[END_REF].

In this article we first present the knowledge base used to achieve this indexing in French langage, i.e. the lexical network JeuxDeMots, then we describe precisely what an enlarged index relative to raw index is, and the index enlargement algorithm based on a spread over the lexical network. Finally we discuss experiments and analyze the results.

Index Enlargement and Spreading

The knowledge base on which our radiological reports indexing strategy relies is the lexical network JeuxDeMots [START_REF] Lafourcade | Making people play for lexical acquisition with the JeuxDeMots prototype[END_REF]. Although this network is general, it contains many specialty data, including medicine/radiology, which we have added within the framework of IMAIOS project. The network is the basis for a propagation algorithm that aims to increase the raw index obtained through conventional methods of information retrieval.

The JeuxDeMots Lexical Network

JDM network is a lexical-semantic graph for the French language whose lexical relations are generated both through GWAP (Games With A Purpose, see [START_REF] Lafourcade | Games with a Purpose (GWAPS)[END_REF]) and via a contributory tool called Diko (manual insertion and automatic inferences with validations). At the time of this writing, the JDM network contains over 20 million relations between around 500,000 terms. The properties of this network that are important in the context of our work are the following:

 among about 80 lexico-semantic relations of the network, those which are relevant for our indexation project are the relations essentially semantic like hyperonymy, typical features, typical places, typical parts, target, etc.;

 polysemous terms are connected by the relation "refinement" with their various senses. About 9,000 polysemous terms are linked to approximately 25,000 meanings.

Figure 1. Screenshot of the contributory tool Diko showing the entry "tibia fracture". Diko is the online tool of visualization and of contribution of the JeuxDeMots lexical network. Note that the entry tibia fracture includes both specific medical relations (such as symptoms, diagnostic ...) and more general associations (such as causes, consequences, etc.).

For example, fracture → fracture (injury), fracture (break), fracture (sociology). The term in brackets is a gloss that allows to know or guess the meaning (refinement) of the polysemous word;

 relations are weighted, the weight reflects the strength of association between terms. Approximately 70,000 relations have negative weights, indicating a wrong relation (wrong relations are kept as they may be interesting within the framework of lexical disambiguation). An example is : *fracture du tibia hypernym (< 0) fracture (sociology: social dislocation) ;

 when a term t is associated with one of the meanings of a polysemous term, there is a relation of inhibition between the other meanings and the term t. For example: fracture inhibition talus (inclination), talus (printing), talus (embankment), astragale (architecture), astragale (botany), There is at least another meaning of talus or of astragale which are related to the term fracture: talus (os) and astragale (os).

The indexing of keywords in the medical field is often limited to certain aspects of a disease [START_REF] Andrade | Automated extraction of information in molecular biology[END_REF] or to a part of the anatomy. But as the purpose of this indexation is to retrieve documents using also everyday language, we index not only anatomical terms (knee, anterior wall of the colon, the genu of the corpus callosum, ...), clinical signs (plantar reflex) and the names of diseases (carcinoma), but also everyday words (fall in the bathtub) likely to be used by the radiologist in his query.

The following table provides an order of size of the amount of information we have at our disposal about the specialty areas that are particularly relevant in the IMAIOS project:

Standard Indexing Report

Our corpus includes approximately 40,000 radiology reports (Example 1) concerning the different medical imaging techniques (MRI, scanner, ultrasonography, X-ray radiology, vascular radiology, scintigraphy ...). These reports are written in semi-structured way: they are generally divided into four parts (indications, technique, results, and an optional conclusion). Each part is written by the radiologist in a very free style, often with a profusion of acronyms (ATCD for of antecedent, ACR for American College of Radiology, tt for treatment, etc.), of elisions (the anterior communicating instead of the anterior communicating artery), and all sorts of various improprieties (influenza instead of influenza virus). Reports contain a lot of implicit information which need to be explicit to realize an indexation meeting the needs of practitioners. For instance it may be very interesting to explicit the expression middle cerebral artery territory.

The creation of the index starting from the reports is made by the traditional methods of information retrieval, i.e. term frequency (TF) and document frequency (DF) to calculate the IDF (Inverse Document Frequency). The identification of the compound terms is made upstream compared to the content of JeuxDeMots network. We use the underscore to separate the two parts of a compound word so that it is considered as an entity at the time of the extraction (tib-ia_fracture).

indications : fracture du tibia droit, chute de ski technique : une série de coupes axiales transverses sur l'ensemble de la cheville droite sans injection de produit de contraste étude : en fenêtres parties molles et osseuses. résultats : fractures diaphysaires spiroïdes à trois fragments principaux du 1/3 distal du tibia et de la fibula avec discret déplacement vers l'avant, sans retrait de refend articulaire. Fractures de la base de M2 et de M3 non articulaire et non déplacée. Fracture articulaire de la partie interne de la base de M1 non déplacée. Atrophie avec dégénérescence marquée des corps musculaires de l'ensemble des loges.

atrophie • cheville • chute • corps mus- culaire • coupe axiale transverse • dégénérescence • déplacement • fibula • fracture • fracture du tibia • loge • non articu- laire • non déplacée • ski • spiroïde • tibia
Example 1: typical radiological report (left) and raw index (right). The raw index is the list of extracted terms, ordered alphabetically (the weights are not mentioned and the list is simplified). Compound words are extracted if they are present in the same form in the JDM network.

Despite the frequency filtering, we keep the words in the vicinity of medicine, even for low TF-IDF values. If a word of the report is connected to medicine (neighbor at a distance of 1) in the JDM network, then it is added to the index. In the same way, non-medical words (motorcycle accident, influence of drugs) are captured and added to the index if they are linked to a term itself linked to medicine (neighbor at a distance of 2): thus motorcycle accident is added because it is linked to polytrauma through the consequence relation and polytraumat is itself related to medicine through the field relation Moreover, if a term of raw index is polysemous, it is interesting to try to determine the proper refinement: for example, in the above report the words fracture (fracture), cheville (ankle), chute (fall) and loge (compartment) are polysemous. We will see later that the identification of proper refinement is important. Moreover, the algorithm does not handle negation but in the phrase "no articular fracture", the term "no articular" will be detected because it belongs to the network JDM.

Thus, the enlargement is a process intended for adding to the index some terms which are relevant, although they are not in the text. Example 2: Enlarged index corresponding to raw index above (the terms are listed alphabetically with the added words in bold). We can see that the general themes of the text are properly identified (medicine, medical imaging, radiology), and the polysemous terms were refined with the correct meaning depending on the context.

Enlargement through Spreading Algorithm

The enlargement strategy is to propagate signals originating from the terms of the raw index over the JDM network. The signal consists in "lighting up" the terms of the raw index within the network and retrieve related terms that light up in their turn.

At each iteration, the terms discharge their current activation to their neighbors. Thus, the total activation is none other than the sum of discharges received by a term during the entire process. For negatively weighted relations (i.e. inhibitory relations), the activation is removed instead of added. A term with negative CA cannot discharge. Iterated sequence is performed synchronously for all terms. Note that the distribution of the signal is proportional to the logarithm of the weight (and not proportional to the weight itself). Specifically, we can describe the algorithm informally as follows:

Init:

terms T of network are associated with a pair of values (CA, TA), current activation and total activation. Algorithm 1: calculation of an enlarged index starting from a raw index, using a propagation over the JDM lexical network. The two main parameters are NBI (number of iterations) and S (% of retained surface for the filter).

Table 3 : The nouv/pert values depending on NBI and S parameters. NBI is the number of iterations performed in the lexical network. S is the retained part of the area under the curve of the cumulative weights of terms reached by the propagation algorithm.

After the iterations (lines 5-7), we obtain a weighted list of terms that are then ranked in order of decreasing weights. We retain by filtering N terms of the highest weight, such that the sum of their weights is S% of the total weight of the terms of the list.

We chose not to use all relations available in the lexical network JDM; indeed, some of them are too lexical: in the context of our work, they could degrade accuracy. We use the following relations (Table 2) (their relative importance, if different from 1 (default weighting) is indicated in brackets): associated ideas (weight of 1/2), hypernyms (weight of 2), synonyms, typical features, symptoms, diagnostics, parts/whole, typical place, causes, consequences, field, and frequently associated with. In the above algorithm, for simplicity, all relations are equally important (default weighting of 1, otherwise, their relative importance should be placed on both sides of the fraction).

Evaluation of Enlarged Index

We conducted a statistical evaluation of our propagation algorithm by randomly selecting 200 enlarged indexes (from a total of 30,000 calculated). We manually reviewed every term of the enlarged index to determine whether it was relevant or not. A relevant term is a term that is considered as appropriate for the description of the report. The presence of irrelevant terms increases the amount of noise when requesting documents. The absence of relevant terms decreases recall.

The pairs of values in Table 3 are nouv/pert, where nouv is the average number of terms of enlarged index that are not in the raw index and pert the average percentage of the relevant terms in enlarged index.

In practice, the pert value is assessed manually just once, regardless of the NBI and S parameters. Indeed we examine for each report all terms obtained in order of decreasing weights, for all parameter values, and then we assess the adequacy of each term. If we find a succession of 5 irrelevant terms, it is considered that the following are also irrelevant. The nouv value can be calculated automatically. For the same number of iterations, the greater the retained part is, the larger the number of terms is (low filtering). This means that if the recall is more important, in consideration accuracy tends to decrease (or even to collapse beyond 30%), because the terms added to the raw index are less and less relevant. Conversely, the more the number of iterations increases, the more the relevant terms are likely to be often reached and through multiple paths starting from the terms of the raw index, thus to be reinforced. The lexical network contains loops (direct and indirect) that act as self-reinforcement structures. The computation time dramatically increases with each new iteration, as the number of terms discharging their activation increases very strongly. For NBI = 5, almost the entire network is reached (if we exclude filtering S), its diameter being of about 6 (JDM is small-world network). Overall the conditions that seem most interesting for a reasonable computation time (a few seconds) are 3-4 iterations and an area of less than 30%.

All ambiguous terms have correctly been disambiguated. This means that the enlarged index systematically included proper refinement when refinement was proposed (this is not necessarily the case for low values of NBI and S). If we recalculate the enlarged index while preventing access to refined terms, the pert value decline globally by 10%, regardless of the values of NBI and S. Try to select the correct meaning of ambiguous words can be carried out jointly with the selection of relevant terms and would even tend to favor it. Finally, all the specialty areas identified by the algorithm turned out relevant. Add the relevant specialties in the raw index before the enlargement process does not significantly improve the results (nor degrade them). Note that if we recalculate the raw and enlarged index while giving access, during propagation, only to immediate neighbors of the word medicine, whatever the relation, then the pert value decline in average by 12%. The use of a wide knowledge base, no limited to the only specialty field would thus improve largely the relevance of the produced index. Thus, the choice not to separate specialized and common vocabulary proves judicious and effective regarding the analysis of radiological reports. Moreover the algorithm is fast and well suited to handle the amount of data generated daily in radiology centers.

One can also notice that the overall process presented above works thematically on the text and semantically on the lexical network. A sharp semantic analysis of the reports would in all likelihood involve a chunk and dependencies analysis. The errors we found (23 terms for 200 indexes, which represent 23 errors for about 10,000 terms) may have different causes:

 lack of information in the knowledge base (20% of error cases);

 lack of semantic role, implying the need for a detailed analysis (55%);

 chimerism -two parts of the report have brought about an irrelevant term (25%).

As mentioned above, the network is characterized by a never ending learning approach (adding relations and refinement occurs permanently) in the spirit of [START_REF] Carlson | Toward an architecture for never-ending language learning[END_REF]. We can therefore reasonably hope that the knowledge base being constantly enriching, errors due to lack of knowledge will rapidly decrease over time. Similarly, the ability to identify the semantic relations and especially semantic roles within the reports would minimize the 2nd and 3rd source of errors.

Conclusion and prospects

Our objective is to automatically index radiological reports, using not only the medical terms but also words of common language that may be included in users' queries, especially hospital practitioners. To increase recall without significantly degrading the accuracy, we add in the raw index some implied words or expressions, using the JeuxDeMots lexical-semantic network as a support of knowledge. As far as we know, very few studies take into account the non-medical items in the radiological reports or carry out implicit inference for identify relevant terms. Conventional approaches to improve recall consist primarily of adding some terms more general (hyperonyms) starting from a medical ontology. But it is tangible that when information of general sense is present, the results are improved: the assumption that it would be better not to separate general knowledge and specialized one seems to be confirmed, at least in the context of our indexing works.

The results presented here are preliminary and require a substantive assessment of indexes on the whole corpus. These first results look promising, but we need to be able to automate the evaluation in order to do it on a larger scale. We could then further analyze the reports by seeking to extract relations between words using analogy/comparison with the relations of the JeuxDeMots lexical-semantic network. Another improvement would be to develop automated means for recognizing negation. So, the indexation would concern not only the terms, but also the semantic relations between them. One objective of the IMAIOS project is also to extract from medical reports some new knowledge to enrich the lexical network. Finally, we also plan to deduct from the corpus some rules of inference and thus make an authentic reasoning, i.e. to propose by deduction and by induction new medical information or even diagnosis.

  accident de ski • accident de sports d'hiver • atrophie • cheville • cheville>anatomie • chute • chute>tomber • corps musculaire • coupe axiale transverse • dégénérescence • dégénérescence musculaire • déplacement • fibula • fracture • fracture articulaire • fracture des membres inférieurs • fracture multiple • fracture diaphysaire • fracture du tibia • fracture non articulaire • fracture non déplacée • fracture spiroïde • fracture avec déplacement • fracture>lésion • imagerie médicale • jambe • lésion • lésion osseuse • loge • loge>anatomie • médecine • non articulaire • non déplacée • péroné • radiologie • ski • spiroïde • sports d'hiver • tibia • traumatisme des membres inférieurs • …

  T of network having neighbors {t 1 ,… ,t n } via a relation r from T to t i with a positive weight w i , we modify CA and TA of : filtered using a percentage of surface S; the remaining activated terms are returned.

Table 1 :

 1 Number of relations of some key terms within the JDM lexical network.

	Term	Outgoing	Incoming
		links	links
	medicine	21408	22666
	anatomy	10477	11453
	radiology	382	502
	accident	741	956
	medical imaging	541	556