Mazen Alsarem

Pierre-Edouard Portier

Sylvie Calabretto

Harald Kosch

Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets

The advances of the Linked Open Data (LOD) initiative are giving rise to a more structured Web of data. Indeed, a few datasets act as hubs (e.g., DBpedia) connecting many other datasets. They also made possible new Web services for entity detection inside plain text (e.g., DBpedia Spotlight), thus allowing for new applications that can benefit from a combination of the Web of documents and the Web of data. To ease the emergence of these new applications, we propose a querybiased algorithm (LDRANK) for the ranking of web of data resources with associated textual data. Our algorithm combines link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset for the evaluation of query-biased ranking of Web of data resources detected in Web pages. We show that, on this dataset, LDRANK outperforms the state of the art. Finally, we use this algorithm for the construction of semantic snippets of which we evaluate the usefulness with a crowdsourcing-based approach.

Introduction

In this work, we introduce LDRANK (see section 4), an efficient query-biased and context-aware ranking algorithm that applies to the resources of a LOD graph. When combined with the automatic annotation of resources in Web pages (e.g. through DBpedia Spotlight [START_REF] Mendes | Dbpedia spotlight: shedding light on the web of documents[END_REF]), LDRANK offers the opportunity to build useful semantic snippet that can apply to any Web page regardless of its provenance (see section 5). In this introduction, we provide the background information from which the necessity for this new algorithm will appear.

On the web of documents links are indications of a relationship between information carried by the documents. Although these indications are coarse-grained, they revealed themselves as essential for the most-effective ranking algorithms (PageRank [START_REF] Page | The pagerank citation ranking: Bringing order to the web[END_REF], HITS [START_REF] Kleinberg | Authoritative sources in a hyperlinked environment[END_REF], SALSA [START_REF] Lempel | Salsa: the stochastic approach for link-structure analysis[END_REF]).

On the web of data, links are fine-grained explicit relationships between resources (i.e., URI for things of the phenomenal world, be they mental or physical). The vast majority of the existing ranking strategies for the web of data (see [START_REF] Roa-Valverde | A survey of approaches for ranking on the web of data[END_REF] and [START_REF] Jindal | A review of ranking approaches for semantic search on web[END_REF] for recent surveys) are relying on adaptations of PageRank.

The modifications made to adapt the PageRank algorithm to the web of data are necessary due to the high heterogeneity of both the provenance of the datasets and the types of the relationships. Otherwise, there are also a few experiments with learning-to-rank approaches applied to the web of data (e.g., [START_REF] Dali | Query-independent learning to rank for rdf entity search[END_REF]). These techniques depend on the availability of relevance judgments for training (although indirect measures of correlated quantities can sometimes be used, e.g. the number of visits agents made to a resource).

In order to manage the aforementioned intrinsic heterogeneity of the web of data, the Linked Open Data (LOD) initiative promotes simple principles for publishing resources in a way conducive to a web of linked data with shared knowledge expressed in a common formalism (RDF) and accessible through a common interface (HTTP). As a key use-case, DBpedia has been used in conjunction with NLP strategies in order to associate resources with their surface forms in a text document. The main current applications for this use-case are: DBpedia Spotlight [START_REF] Mendes | Dbpedia spotlight: shedding light on the web of documents[END_REF], AlchemyAPI3 (similar to DBpedia Spotlight, but finds resources in various LOD datasets and thus includes a coreference resolution step), OpenCalais 3 , SemanticAPI from Ontos 3 , ZenCrowd [START_REF] Demartini | Zencrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking[END_REF]. . .

In this context, we address the problem of ranking resources that come from the automatic annotation of a Web page selected by a web search engine in response to a user query. The main challenge is to make good use of the knowledge given by the query and the Web page's text in order to palliate the sparsity and heterogeneity of the graph of resources. We propose an algorithm, LDRANK, and we compare it to other modified PageRank algorithms. Moreover, we apply it to the construction of semantic snippets 4 . A snippet is an excerpt from a Web page determined at query-time and used to express how a Web page may be relevant to the query. A semantic snippet is meant to improve the process of matching the ranked Web pages presented within a Search Engine Result Page (SERP) with the user's mental model of her information need. It achieves this objective by making apparent the relationships existing between the information need and the more relevant resources present in the Web page.

In section 2 we introduce the related works about enhanced snippets for the web of documents and for the web of data. In section 3, we describe the construction of a dataset for the evaluation query-biased entity ranking algorithms. In section 4 we present the LDRANK algorithm and its evaluation. In section 5, we introduce ENsEN, the software system we developed to provide semantic snippets. In section 6 we present the results of an evaluation of the usefulness of ENsEN.

Related Works

We first mention works that generate snippets for native RDF documents. Ge et al. [START_REF] Ge | Incorporating compactness to generate termassociation view snippets for ontology search[END_REF], and Penin et al. [START_REF] Penin | Snippet generation for semantic web search engines[END_REF] focus on the generation of snippets for ontology search. Bai et al. [START_REF] Bai | Rdf snippets for semantic web search engines[END_REF] generate snippets for a semantic web search engine.

In [START_REF] Penin | Snippet generation for semantic web search engines[END_REF], the authors first identify a topic thanks to an off-line hierarchical clustering algorithm. Next, they compute a list of RDF sentences (i.e. sets of connected RDF statements) semantically close to the topic. Finally, they rank the selected RDF statements by considering both structural properties of the RDF graph and lexical features of the terms present in the ontology (by way of a Wordnet-based similarity measure).

In [START_REF] Ge | Incorporating compactness to generate termassociation view snippets for ontology search[END_REF], the authors first transform the RDF graph into a term association graph in which each edge is associated with a set of RDF sentences. Their objective is to produce a compact representation of the relationships existing between the terms of the query. These relationships are to be found in the RDF graph. To do this, they decompose the term association graph into maximum r-radius components in order to avoid long distance relations between query terms. Next, they search sub-snippets in these components (i.e. connected subgraphs that link some of the query-terms). Finally, they select some of the sub-snippets to form the final snippet.

In [START_REF] Bai | Rdf snippets for semantic web search engines[END_REF], the authors first assign a topic to the RDF document (they use a property such as p:primaryTopic if it exists, otherwise they rely on a heuristic based on the comparison of the URI of the candidates topic-nodes with the text of the URL of the RDF document). Next they design a ranking algorithm for RDF statements. Particularly, they introduce the notions of correlative (e.g. foaf:surname and foaf:family name) and exclusive (e.g. foaf:name and foaf:surname) properties. Finally, they use this ranking algorithm to give the user a set of relationships between the query-related statements and the topicrelated statements.

To sum up, we agree with Ge et al. [START_REF] Ge | Incorporating compactness to generate termassociation view snippets for ontology search[END_REF] that the main benefit of possessing highly structured data from an RDF graph is the possibility to find non-trivial relationships among the query terms themselves, and also between the query terms and the main concepts of the document. Moreover, we agree with Penin et al. [START_REF] Penin | Snippet generation for semantic web search engines[END_REF] and Bai et al. [START_REF] Bai | Rdf snippets for semantic web search engines[END_REF] about the necessity to design a ranking algorithm for RDF statements that considers both the structure of the RDF graph and lexical properties of the textual data. However, we find ourselves in an inverted situation with genuine text extracted from classical Web pages, and RDF graphs automatically generated from these Web pages.

Indeed, LOD resources can either come from: (i) a LOD dataset (e.g. by way of SPARQL queries), (ii) semantic annotations embedded in a Web page (i.e., by using RDFa, Microdata, or Microformats5 ), or (iii) automatic association of resources with surface forms of the Web page by way of NLP strategies (e.g. DBpedia Spotlight [START_REF] Mendes | Dbpedia spotlight: shedding light on the web of documents[END_REF], ZenCrowd [START_REF] Demartini | Zencrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking[END_REF],. . . ). Among the approaches that offer to enhance the snippets of a SERP by using the web of data [START_REF] Haas | Enhanced results for web search[END_REF] [START_REF] Steiner | How google is using linked data today and vision for tomorrow[END_REF], none rely on automatic annotation: they use only embedded annotations. Haas et al. [START_REF] Haas | Enhanced results for web search[END_REF] employed structured metadata (i.e. RDFa and several microformats) and information extraction techniques (i.e. handwritten or machine-learned wrappers designed for the top host names e.g., en.wikipedia.org, youtube.com,. . . ) to enhance the SERP with multimedia elements, key-value pairs and interactive features. By combining metadata authored by the documents' publishers with structured data extracted by ad-hoc wrappers designed for a few top host names, they are able to build enhanced snippets for many results of a SERP. They chose not to use the LOD graph to avoid the problem of the transfer of trust between the Web of documents and the Web of Data. Indeed, they argue that the quality of the editorial processes that produce the Web of Data from the Web of documents (e.g. the transformation from Wikipedia to DBPedia) cannot be controlled. Therefore, from their point of view, making use of the LOD graph for enhancing snippets would introduce too much noise. Also, Google Rich Snippet (GRS) [START_REF] Steiner | How google is using linked data today and vision for tomorrow[END_REF] is a similar initiative that relies exclusively on structured metadata authored by the Web pages' publishers.

Moreover, a study made in 2012 [START_REF] Bizer | Deployment of rdfa, microdata, and microformats on the web-a quantitative analysis[END_REF] on the over 40 million websites of the Common Crawl corpus6 shows that 5.64% of the websites contained embedded structured data. However, nearly 50% of the top 10,000 websites of the Alexa list of popular websites7 had structured data. Moreover, the authors of the study say that: "The topics of the data [. . . ] seem to be largely determined by the major consumers the data is targeted at: Google, Facebook, Yahoo!, and Bing". Therefore, there is still a clear need for a high quality process that, given a document relevant to a Web search query, can select the most relevant resources among those automatically discovered within the document (e.g., through state of the art NLP algorithms), and this, whatever the document's provenance may be. An efficient algorithm for ranking the resources of a LOD graph while taking into account their textual context could serve this purpose.

However, most of the existing approaches that can be used to rank the resources of graphs coming from the Web of data are not well adapted to this task. Thus, OntologyRank [START_REF] Ding | Swoogle: a search and metadata engine for the semantic web[END_REF] (used by Swoogle) introduces a modified version of PageRank with a teleportation matrix that takes into account the types of the links between ontologies. Similarly, PopRank [START_REF] Nie | Object-level ranking: bringing order to web objects[END_REF] offers a modified PageRank that considers the different types of predicates between resources. RareRank [START_REF] Wei | Rational research model for ranking semantic entities[END_REF] introduces a modified PageRank with a teleportation matrix that takes into account topical relationships between resources as available from ontologies. The approach introduced in [START_REF] Fafalios | Post-analysis of keyword-based search results using entity mining, linked data, and link analysis at query time[END_REF] modifies the teleportation matrix by taking into account the ranking of the Web pages within which the resources were discovered. Since this approach can be applied to our context, we include it to our evaluations (see section 4.6). Finally, TRank [START_REF] Tonon | Trank: ranking entity types using the web of data[END_REF] addresses the task of ranking entity types given an initial entity and its textual context.

Given this context, we introduce LDRANK, a query-biased and contextaware ranking algorithm for LOD resources. Moreover, we apply LDRANK to the construction of generic semantic snippets that can apply to any Web page. In the next section, we introduce how we built a dataset through crowdsourced relevance judgments to evaluate our algorithm, LDRANK.

Dataset for Evaluating Query-biased Ranking of LOD resources

We are interested in query-biased algorithms for the ranking of resources in sparse and heterogeneous LOD graphs associated with a textual context. To our knowledge, there is no evaluation dataset suited to this context (this can be verified for example through a recent survey [START_REF] Roa-Valverde | A survey of approaches for ranking on the web of data[END_REF]). Therefore, we used a crowdsourcing approach for making our evaluation dataset (freely available online 8 ).

We now describe how this dataset was obtained.

Data Collection

We took randomly 30 queries from the "Yahoo! Search Query Tiny Sample" offered by Yahoo! Webscope9 . We submitted the queries to the Google search engine and we kept the top-5 Web pages for each query. For each one of the 150 HTML Web pages, we extracted its main raw textual content by applying the algorithm proposed by Kohlschtter, Fankhauser, and Nejdl [START_REF] Kohlschütter | Boilerplate detection using shallow text features[END_REF]. On average, the text we kept for each Web page is made of 467 words. We applied DBpedia Spotlight [START_REF] Mendes | Dbpedia spotlight: shedding light on the web of documents[END_REF] on these texts to detect resources. There are on average 81 detected resources by Web page.

Microtasks Generation

Considering the length of our texts, the task of evaluating all the annotations of a Web page would be too demanding. Therefore, we divide this task into smaller "microtasks". A microtask will consist in scoring the relevance of the annotations of a single sentence. We split the text of a Web page into sentences with the ICU BreakIterator algorithm 10 . There are on average 22 sentences by document. Moreover, if a sentence contains more than 10 annotated resources, the work will be split over multiple microtasks. We used the CrowdFlower11 crowdsourcing platform. It distributes work to contributors in the U.S. and 153 other countries while maintaining quality and controlling costs. It has a global pool of 5 million contributors. A microtask is called a job by CrowdFlower. The design of a job is specified in CML, a markup language provided by CrowdFlower. For each job, we give the worker a short list of instructions about how to complete the job (we tested many formulations until finding a suitable one understood by all workers). We provide the worker with a topic made of a title (the query) and a short text (the sentence). For each resource in the sentence, there is a question asking the worker to evaluate the correctness and the relevance of the annotation. We used the ordinal scale proposed by Järvelin and Kekäläinen when they introduced the DCG graded relevance [START_REF] Järvelin | Ir evaluation methods for retrieving highly relevant documents[END_REF]: irrelevant (0), marginally relevant (1), fairly relevant

(2), and highly relevant [START_REF] Berry | Large-scale sparse singular value computations[END_REF]. Each question is associated with a small text that describe the resource (viz. the beginning of its DBpedia abstract). Each job was given to 10 workers. Therefore, for each job we have 10 judgments. Each job was paid $.01.

Quality Control

We only accepted workers that had completed over a hundred questions across a variety of job types and had an high overall accuracy. Workers had a maximum of 30 minutes to provide an answer. Workers had to spend at least 10 seconds on the job before giving an answer. We measured the agreement between workers with the Krippendorff's alpha coefficient [START_REF] Krippendorff | Content analysis: An introduction to its methodology[END_REF]. This coefficient uses by default a binary distance to compare answers, but other distances can be used. To take into account the fact that we used an ordinal scale encoding both correctness and relevance, we used the following symmetric distance: d(0, 1) = 0.5 ; d(0, 2) = 0.75 ; d(0, 3) = 1 ; d(1, 2) = 0.25 ; d(1; 3) = 0.5 ; d(2; 3) = 0.25 ; d(x, x) = 0. With these parameters, we obtained an alpha of 0.22. According to Landis and Koch's scale [START_REF] Landis | The measurement of observer agreement for categorical data[END_REF], this can be considered a fair agreement (the scale was designed for Fleiss' kappa, but the Krippendorff's alpha is in most ways compatible with the kappa). However, by comparison with existing works that applied crowdsourcing to an information retrieval context, we cannot be satisfied with an alpha of 0.22. For example, Jeong et al. [START_REF] Jeong | A crowd-powered socially embedded search engine[END_REF] obtained a Fleiss' kappa of 0.41 (i.e. moderate agreement) for a crowd-powered socially embedded search engine. However, Alonso, Marshall, and Najork [START_REF] Alonso | Crowdsourcing a subjective labeling task: A human-centered framework to ensure reliable results[END_REF] obtained a Krippendorff's alpha between 0.03 and 0.19 for a more subjective task: deciding if a tweet is or is not interesting.

To improve the quality of our dataset, we found the workers that often disagreed with the majority. In fact, by removing the workers that disagree with the majority in more than 41.2% of the cases, we obtained a Krippendorff's alpha of 0.46. Then, 96.5% of the jobs are done by at least 3 workers, 66% of the jobs are done by at least 5 workers, and we have only 0.7% of the jobs done by only 1 worker.

Aggregation of the Results

We used majority voting for aggregating the results within each sentence. We used two different methods to break ties : (i) the maximum of the mean of the workers' trust (a metric provided by CrowdFlower), or (ii) the highest value. We discovered later that these two choices result in very similar outcomes when the dataset is used to compare ranking algorithms. We used the same majority voting strategy to aggregate the results at the level of a Web page.

In the next section, we introduce LDRANK, a query-biased ranking algorithm for LOD resources. The dataset we just described will be used in section 4.6 to evaluate LDRANK and to compare it to the state of the art.

LDRANK, a Query-biased Ranking Algorithm for LOD Resources

Context

We introduce LDRANK (Linked Data Ranking Algorithm), a quey-biased algorithm for ranking the resources of a RDF graph. We suppose that the resources were discovered in a Web page found by a Web search engine in answer to a user's query.

In our experiments, the resources are detected in the Web page by DBpedia Spotlight [START_REF] Mendes | Dbpedia spotlight: shedding light on the web of documents[END_REF]. From this set of resources and through queries to a DBpedia SPARQL endpoint, we obtain a graph by finding all the relationships between the resources. To each resource, we associate a text obtained by merging its DBpedia abstract and windows of text (300 characters) from the Web page centered on the surface forms associated with the resource. We remove the empty words and we apply stemming 12 to this text.

LDRANK is adapted by design to such sparse graphs of LOD resources detected in a Web page. First, LDRANK uses the explicit structure of the graph through a PageRank-like algorithm; second, it uses the implicit relationships that can be inferred from the text associated with the resources through an original variation of the Singular Value Decomposition (SVD); and third, it takes into account the ranking of the Web pages where the resources were found thanks to a scoring function first introduced by Fafalios and Tzitzikas [START_REF] Fafalios | Post-analysis of keyword-based search results using entity mining, linked data, and link analysis at query time[END_REF].

More precisely, the SVD-based textual analysis and the exploitation of the ranking obtained from a Web search engine result page, each produce a different probability vector expressing some prior knowledge (or belief) about the importance of the resources (see sections 4.2 and 4.3). Next, these probability vectors are combined through a consensual linear opinion aggregation strategy first introduced by Carvalho and Larson [START_REF] Carvalho | A consensual linear opinion pool[END_REF] (see section 4.4). Finally, we use this combined prior knowledge to influence the convergence of a PageRank-like algorithm towards a stable probability distribution corresponding to the final ranking of the resources (see section 4.5).

Prior Knowledge Based on a Web Search Engine Result Page

Algorithm H (Hit Score). This algorithm computes a probability vector (hitdistrib) that represents prior knowledge about the importance of the resources based on the rank of the Web pages in which they were detected. This strategy was first introduced by Fafalios and Tzitzikas [START_REF] Fafalios | Post-analysis of keyword-based search results using entity mining, linked data, and link analysis at query time[END_REF]. H1. A ← the list of the top Web pages ranked by a Web search engine. H2. E ← the set of detected resources. H3. docs(e) ≡ the documents of A containing the detected resources e. H4. rank(a) ≡ the rank of document a in A.

We shall now introduce the essential property of the SVD on which relies Algorithm S. For a strong dimensional reduction (i.e. for small values of k), the transformation S k U T tends to place resources that were orthogonal to many other resources in the row space of R near the origin of the k-dimensional resulting space. Indeed, as we said above, the SVD can be seen as an optimization algorithm, and to minimize the error due to the impossibility for a resource to be orthogonal to more than k non co-linear resources, this resource should be placed as close to the origin as possible for its dot product with other resources to remain small. A similar argument can be used to show that resources co-linear to other resources in the row space of R will also tend to be near the origin of the k-dimensional space.

Algorithm S uses this property for ranking the resources by their importance relatively to the user's information need. In R the resources that are believed to be close to the information need are given artificially more importance. Therefore, resources having interesting relationships with the resources artificially pushed away from the origin will also move away from the origin. By "interesting", we mean different from the relationships they maintain with much of the other resources (cf. the geometric argument developed above about the SVD seen as an optimization algorithm).

We obtained the best experimental results with a reduction to the 1 dimensional line (i.e. with nb dim = 1 in steps S3 and S7 of Algorithm S), and with a stress factor (step S6 of Algorithm S) of 1000.0.

Belief Aggregation Strategy

We consider hitdistrib (from Algorithm H), svddistrib (from Algorithm S), and the equiprobable distribution (equidistrib) as three experts' beliefs (or prior knowledge) about the importance of the resources. To aggregate these beliefs, we apply Carvalho and Larson [START_REF] Carvalho | A consensual linear opinion pool[END_REF] consensual linear opinion pool algorithm. It is an iterative algorithm where at each step expert i re-evaluates its distribution as a linear combination of the distributions of all the experts. The weight associated by expert i to the distribution of expert j is proportional to the distance between the two distributions. The authors define this distance such that the process converges towards a consensus. We will refer to this resulting consensual probability vector by the name f inaldistrib.

LDRANK

The PageRank [START_REF] Page | The pagerank citation ranking: Bringing order to the web[END_REF] algorithm transforms the adjacency matrix (M ) of a network of Web pages into a matrix H which is both stochastic (i.e., each row of H sums to 1) and primitive (i.e., ∃k s.t. H k > 0), thus assuring the existence of a stationary vector (i.e., the positive eigenvector corresponding to the eigenvalue 1). This stationary vector is a probability vector that can been interpreted as representing the importance of each Web page. Moreover, it can be computed efficiently with the power iteration algorithm by taking into account the sparsity of the stochastic matrix.

In the original version of the PageRank algorithm, no assumption is made about the probability of importance of the Web pages before the link analysis takes place. In other words: first, the matrix M is transformed into a stochastic matrix S by replacing each null row by the equiprobable distribution (equidistrib); second, the matrix S is transformed into a primitive matrix H by a linear combination with the so-called teleportation matrix (T ): H = αS + (1 -α)T where each row of T is the equiprobable distribution (equidistrib).

In algorithm LDRANK, instead of using the equiprobable distribution, we use the consensual distribution (f inaldistrib) introduced above in section 4.4. We obtained the best experimental results for 0.6 ≤ α ≤ 0.8. Moreover, we set at 1E -10 the value of the convergence threshold controlling the termination of the power iteration method that computes the stationary vector.

LDRANK is available online under an open-source license14 .

LDRANK evaluation

We compared four ranking strategies, each one of them is based on a different source of prior knowledge used to inform a PageRank-like algorithm: unmodified PageRank i.e., prior knowledge about the importance of the resources is modeled by an equiprobable distribution (we name this strategy EQUI); PageRank modified with the hitscore prior knowledge introduced in section 4.2 and due to Fafalios and Tzitzikas [START_REF] Fafalios | Post-analysis of keyword-based search results using entity mining, linked data, and link analysis at query time[END_REF] (named HIT); PageRank modified with our new SVD-based prior knowledge introduced in section 4.3 (named SVD); and PageRank modified with a consensual mixture of the three previous sources of prior knowledge (named LDRANK).

In order to compare the four strategies (EQUI, HIT, SVD and LDRANK), we used the NDCG (Normalized Discounted Cumulative Gain) metric. The DCG (Discounted Cumulative Gain) at rank r is defined as: DCG r = rel 1 + r i=1 reli log2i . NDCG at rank r is DCG at rank r normalized by the ideal ranking at rank r. The construction of the dataset used for the evaluation was introduced in section 3.

The results are presented in Figure 1. We can see that the SVD and HIT strategies obtain similar performances. However, they are clearly outperformed by their consensual combination. Moreover, since we systematically took into account the sparsity of the data, we obtain good execution time performances (see Figure 2). The SVD strategy takes more time than the HIT strategy since it needs to compute the SVD. The additional time spent by the combined strategy is due to the time necessary to converge towards a consensus. Finally, we did similar experiments by considering the edges of the graph bidirectional. The relative performance and accuracy of the algorithms were similar, but the absolute NDCG scores were slightly better.

It should be noted that through these experiments, beside introducing a new efficient ranking strategy based on an original use of the SVD dimensionality reduction, we are also offering evidence that different strategies based on a modification of the teleportation matrix of the PageRank algorithm can profitably be combined when considered as concurrent sources of prior knowledge about the importance of the resources. In order to better convince the reader of the usefulness and efficiency of LDRANK, we used it at the core of ENsEN (Enhanced Search Engine): a software system that enhances a SERP with semantic snippets (a live demonstration is available online, see a previous footnote for the URL). Given the query, we obtain the SERP (we used Google for our experiments). For each result of the SERP, we use DBpedia Spotlight to obtain a set of DBpedia resources. In the same way, we find resources from the terms of the query. From this set of resources and through queries to a DBpedia SPARQL endpoint, we obtain a graph by finding all the relationships between the resources. To each resource, we associate a text obtained by merging its DBpedia's abstract and windows of text from the Web page centered on the surface forms associated with the resource. With as input the graph, its associated text, and the resources extracted from the query, we execute LDRANK and we obtain a ranking of the resources. The top-ranked resources (viz. "main-resources") are displayed on the snippet. From a DBpedia SPARQL endpoint, we do a 1-hop extension of the main-resources in order to increase the number of triples among which we will then search for the more important ones. To do this, we build a 3-way tensor from the extended graph: each predicate corresponds to an horizontal slice that represents the adjacency matrix for the restriction of the graph to this predicate. We compute the PARAFAC decomposition of the tensor into a sum of factors (rank-one three-way tensors) and interpret it in manner similar to [START_REF] Franz | Triplerank: Ranking semantic web data by tensor decomposition[END_REF]: for each main-resource, we select the factors to which it contributes the most (as a subject or as an object), and for each one of these factors we select the triples with the best ranked predicates. Thus, we associate to each main-resource a set of triples that will appear within its description. Finally, we used a machine learning approach to select short excerpts of the Web page to be part of the description of each main-resource. In the context of this paper, for lack of space, we cannot describe this process but full details are available in an online technical report (see a previous footnote for the URL).

Crowdsourcing-based User Evaluation

We selected randomly 10 tasks from the "Yahoo! Answers Query To Questions" dataset 15 . Each task was made of three questions on a common topic. To each task corresponds a job on the CrowdFlower platform. Each job was priced $0.20.

We collected 20 judgments for each task. Half of the workers was asked to use our system, and the other half used Google. In order to control that a worker answered the task by using our system, we generated a code that the worker had to copy and paste into her answer. The correctness results are shown on Figure 3. Only complete answers were considered correct. We also monitored the time spent to answer the tasks (see Figure 4). Thus, ENsEN is clearly beneficial to its users in terms of usefulness.

Conclusion

We proposed a new algorithm, LDRANK, for ranking the resources of a sparse LOD RDF graph given the knowledge of a user's information need expressed as a query made of keywords. These kind of graphs appear in particular as the result of the automatic detection of resources in a Web page. LDRANK takes advantage of both the explicit structure given by the Web of data and the implicit relationships that can be found by text analysis of a Web page. We applied LDRANK in the context of semantic snippets where its high accuracy allowed for the construction of useful and usable enhanced snippets that integrate resources obtained from the automatic annotation of a Web page. Future work could evaluate the potential of this approach for exploratory search.

Fig. 1 .

 1 Fig. 1. Comparison of the NDCG scores for the four different strategies

Fig. 2 .

 2 Fig. 2. Comparison of the execution time for the four different strategies (with processor: 2.9 GHz Intel Core i7, and memory: 8 GB 1600 MHz DDR3)

Fig. 3 .

 3 Fig. 3. Average Number of Correct Answers

Fig. 4 .

 4 Fig. 4. Time Spent for Answering the Tasks

www.alchemyapi.com ; www.opencalais.com ; www.ontos.com

www.w3.org/TR/xhtml-rdfa-primer/ ; microformats.org/ ; www.w3.org/TR/microdata/

http://commoncrawl.org

http://www.alexa.com/topsites

http://liris.cnrs.fr/drim/projects/ensen/

http://webscope.sandbox.yahoo.com/catalog.php?datatype=l

http://icu-project.org/apiref/icu4c/classicu 1 1BreakIterator.html

http://www.crowdflower.com/

http://snowball.tartarus.org

Source code available online under an opensource license http://liris.cnrs.fr/drim/projects/ensen/

http://webscope.sandbox.yahoo.com/catalog.php?datatype=l

S2.

[Initial important resources.] inf o need ← a set of resources made of the union of the resources detected in the text of the query and the one resource with the best hitscore (for the case when no resources were detected in the query). We assume that these resources are likely to be close to the information need of the user.

S3.

Since R is very sparse, we use the las2 algorithm developed by Michael W. Berry [START_REF] Berry | Large-scale sparse singular value computations[END_REF] to compute the decomposition:

. from the perspective of the Frobenius norm, R k is the best rank-k approximation of R).

S4.

[Resources' coordinates in the reduced space.] SU T ← SU T In the new k-dimensional space, this operation scales the coordinates of the resources (i.e. the rows of U ) by their corresponding factor in S. This is done by the matrix product: SU T . Thus, we obtain the coordinates of the resources in the reduced space (i.e. the columns of SU T ).

S5. prev norms ← euclidean norms of the resources in the reduced space.

S6. [Updated matrix.] R ← R where the rows corresponding to the resources of inf o need have been multiplied by the parameter stress (since R is in CCS format, it is more convenient to do this operation on the transpose of R).


Quentin Labourey

Denis Pellerin

Michèle Rombaut

Olivier Aycard

Catherine Garbay

Michele Rombaut

Sound classification in indoor environment thanks to belief functions

Keywords: Sound classification, Indoor sounds, Belief functions, Features selection, Reject class

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Intelligent systems equipped with sensors are starting to appear in our homes: home automation, monitoring systems, companion robots, intelligent toys are becoming accessible to everyone. In order to get information about human activity, sensors (typically cameras and microphones) provide substantial amounts of data, but they all require adapted perception algorithms to interact with the user. In this paper, sounds are considered as a source of information, to be mixed with other modalities (video in particular) in the design of ambient monitoring systems (figure 1). Extensive works have been performed as regards the visual modality in indoor environment, but sound classification in those environments is still an open and complex problem. Sounds can be a very good indicator of the content of a scene, or at least give hints as to what the objects of interest in the scene are.

However, the variety of sounds encountered in real-life scenarios, as well as the small quantity of available labelled data to learn from, often lead to a restrained framework of classification: discrimination between speech and music, mu-sic genre differentiation, speaker recognition, footstep detection, etc... The variety of sounds also makes it difficult to propose a hard classification method as a sound coming from the same source can change depending on the situation. Moreover, in case of an interactive or reactive system, misclassification can have serious negative consequences, particularly in the case of monitoring systems.

As a consequence, we propose a classification scheme grounded on two pillars. Firstly, sound should be exploited as a modality that brings specific information, whose role is to complement others. Therefore, there is no need to ground the classification scheme on a wide taxonomy, accounting for any event in the monitored scene. Secondly, the risk of error should be minimized, in order that information provided by this modality may be considered as reliable. Hard classification is then to be avoided. Rather, doubtful events may be classified as such in a "doubt"/"unknown" class and left for further investigation, under complementary modalities. A dedicated taxonomy together with belief functions are proposed in this respect.

PREVIOUS WORKS

Different approaches have been used for sound classification, although the global scheme is oftentimes similar: find a description space that separates the different classes at best and apply classification or separation in that space. Most of the time, finding the right description space to describe the signal at best means extracting features describing the most discriminant characteristics from the signals. Numerous types of classification algorithms can be used.

The literature on sound classification is diverse, in different fields:

• Speech/music discrimination or musical genre discrimination have been extensively explored with various methods, some examples of which are: [START_REF] Tzanetakis | Musical genre classification of audio signals[END_REF] and [START_REF] Langlois | Automatic music genre classification using a hierarchical clustering and a language model approach[END_REF] worked on the discrimination of a music dataset into different genre, using GMM and KNN for classification on various classical features. [START_REF] Haque | An analysis of content-based classification of audio signals using a fuzzy c-means algorithm[END_REF] worked on the differentiation between speech and music, introducing the uncertainty inside his classification with the use of the C-means algorithm. However this uncertainty was not used in the decision step. A more complete and recent review of speech/music discrimination methods can be found in [START_REF] Velayatipour | A review on speechmusic discrimination methods[END_REF].

• Indoor sound classification: The literature is less diverse on the topic, as it is fairly new, however some works have been performed, mainly on recognizing sound-events or particular activities that can be heard in a home: [START_REF] Janvier | Sound-event recognition with a companion humanoid[END_REF] worked on sound-event detection thanks to a humanoid robot with a method based on vector quantization with a very precise taxonomy on particular activities, [START_REF] Vacher | The Sweet-Home speech and multimodal corpus for home automation interaction[END_REF] performed an experiment inside an automated home, where people were supposed to accomplish daily activities during a time and the sound was acquired. An event-detection system followed by a rather simple sound classification was executed, then automatic speech recognition was performed on that database, however the dataset itself is quite complex. Various other works on specific problems exist such as elder fall detection [START_REF] Zigel | A method for automatic fall detection of elderly people using floor vibrations and sound: Proof of concept on human mimicking doll falls[END_REF], footstep classification [START_REF] Itai | Footstep classification using simple speech recognition technique[END_REF], but the present work tries to define a more general framework.

SEMANTIC GRANULARITY OF CLASSES

Most of the works described in the previous section are based on a semantically precise taxonomy [START_REF] Song | Different types of sounds influence gaze differently in videos[END_REF], which can be hindering in some cases:

• Having a lot of precise classes means having to learn the different classes on precise objects of a defined environment. This would cause problems for adapting the system to a new environment.

• The diversity of indoor sounds makes it difficult to chose a granularity when semantic classes are chosen, and even inside a semantic class, the realizations can be diverse, e.g. should we make the difference between a door slamming and a window slamming, or footsteps on a wooden floor and footsteps on tiles ?

• It is important for an intelligent system to make as few errors as possible, particularly in case of monitoring. If two classes are close in terms of features but semantically very different (e.g. a person falling versus a door slammed), it might be better to consider that they are the same class to further investigate it with other sensors such as cameras or depth-sensors.

From these considerations, we decided to consider the following classes. These classes are meant to share a common fate as regards the overall goal of our design, being (i) solely identified on the basis of sound information, and (ii) discriminated with a rather high confidence:

• Speech: In indoor environment, speech is the most important indicator of a person being in presence. It is important for monitoring systems to be able to detect if a person is speaking or not.

• Music: Music is a very common sound that can be heard in indoor environment: radio, television, music is almost everywhere in our everyday life.

• Short indoor sounds (impact): It is difficult to separate the sound of a door slamming, a person falling, the impact of something thrown on a table, etc... However each time an impact is heard, it is important to be able to classify it as it can mean a problem.

• Long indoor sounds: This class regroups all sounds with sparse energy that are longer than an impact: steps, knocking, clothes friction, etc..

• Doubt: As said, the variability and the diversity of sounds that can be heard in indoor environments makes it important to be able to classify an extract as an unknown sound. The source of this type of sounds will require investigation by other sensors.

FEATURES SELECTION

To classify the extracts at best, it is necessary to chose adapted features. This section briefly describes the features that were studied for this work, and the way the signal is finally represented before classification, by selecting the features that separate the classes best individually. The features that were chosen in the final selection are marked with a "*" sign. A more thorough description of the features can be found in [START_REF] Haque | An analysis of content-based classification of audio signals using a fuzzy c-means algorithm[END_REF]. The process of feature extraction is the following one: a sliding hamming window W is applied on the whole signal with overlapping. At each window position, features are extracted. As this represents a lot of data, those features are statistically aggregated to obtain one feature vector per signal, which is the final representation.

Classic sound features

Features in sound analysis are classically divided into 3 broad categories: temporal, spectral and transform features.

In temporal features, one of the most classic is the root mean square energy (E), which represents the quantity of energy associated with a portion of the signal. As such this feature does not give much information about the content of the signal but enables the computation of Low Energy frames (LE)* which is the fraction of frames which energy is lower than a portion of the average root mean square energy across all frames. This feature is efficient to detect the temporal sparsity of energy across the signal. More precisely it can easily discriminate signals with an energy temporally focused at one instant. Another frequently used temporal feature is the Zero Crossing Rate (ZCR), which has been extensively used in the speech/music discrimination framework. It counts the number of time the signal changes its sign inside an analysis window.

In spectral features, the spectral flux (SF)* represents the local temporal variations of the spectrum, while central frequency and bandwidth describe the repartition of the energy in a window of analysis. Spectral Rollof is defined as the quantity of the spectrum containing a defined percentage α of the power spectrum. Most of the time the percentage is chosen between 80% and 95%.

As for transform features, Mel-Frequency Cepstrum Coefficients (MFCC)* are amongst the most commonly used features in speech recognition and discrimination. They are obtained by mapping Fourier coefficients on the Mel-scale thanks to triangular overlapping windows. As for [START_REF] Haque | An analysis of content-based classification of audio signals using a fuzzy c-means algorithm[END_REF], only the first 13 MFCCs are extracted as the first coefficients are known to be the most significant.

Normalization and final representation

To compare the different features, the euclidian distance is used. As the features do not have the same dynamic, it is important to normalize each feature across the whole dataset to avoid favoring a feature with a bigger dynamic during the distance computations.

Each of the cited features are extracted for every window of analysis, which means that for a 10 second signal, with a 20 ms window analysis W, a total of 950 features is extracted from one signal. To obtain a representation of lesser dimension of the signal, we chose to compute for each feature the mean and variance, which leaves us with a representation of the signal by a feature vector s i of size N = 40.

Feature selection

Extracting a lot of features does not mean obtaining better results in the classification. As we are going to base the classification on the euclidean distance between signals in the feature space, we can have a rough idea of how individual features will separate the different classes side-by-side, by computing the mean distance between each pair of class for that features only, and taking into account intra-class mean distance, to choose feature that represents the classes in the most compact way. That is why the following expression is computed for each feature and each pair of classes:

D Ci,Cj = D inter (C i , C j ) D intra (C i ) + D intra (C j ) (1)
where D inter (C i ,C j ) is the mean distance between all pairs of classes C i and C j for the considered feature. Let C i contain N 1 examples x i of the considered feature, and C j contain N 2 examples y j :

D inter = N1 i=1 N2 j=1 |x i -y j | (2)
D intra is the same distance within one class. This enables us to see which features separates the classes best. The higher the distance, the more distinct the classes in the considered feature domain.

An arbitrary number of four features were chosen for the classification amongst those that yielded the higher distance between classes: two MFFC variances for MFCC number 2 and number 4, the spectral flux variance, and a very selective LE selecting the number of frames with a root mean square energy higher than 10 times the average value. It appears that the features that discriminate the classes at best individually are either transform domain features (MFCCs have shown great results in speech and music discrimination in the past), or representative of the temporal repartition of energy in the signal. It makes sense, since our classes are partly based on the sparsity of energy inside the signal. Spectral feature that describes that signal at a precise time like bandwidth or central frequency, might not suffice, as a class can be quite diverse spectrum-wise, while features that describes the evolution of the spectrum in time, such as the spectral flux might be better. Those four features are well adapted to our proposed taxonomy.

CLASSIFICATION SCHEME

As it was stated before, it is important to have as few misclassification as possible: if a person falling is classified as a person speaking by mistake, the system cannot be considered efficient.

The classification algorithm used for this work has to take into account the uncertainty of classification, which means "hard" classification must be ruled out. Moreover, it is important to quantify the ignorance we have about the class of the considered extract: if the system is unable to classify the sound heard, information can be relayed to sensors that might be able to understand the nature or the source of the sound.

Belief functions are particularly adapted to that kind of classification [START_REF] Denoeux | A k-nearest neighbor classification rule based on dempster-shafer theory[END_REF]: they enable the classification system to quantify its ignorance about the considered extract. The classification used here is the belief K-Nearest neighbors: part of the dataset is used as a training set (labelled data), and the rest is used as the validation set.

For each signal s to classify amongst the class C = {{C 1 }• • • {C M }}, the class of its K nearest neighbors x i with i ∈ {1, .., K} is observed. For the i th neighbor of class C q , the basic probability assignment on the subsets of C is built as follows:

m s,i ({C q }) = α exp -γd i (3) m s,i (C) = 1 -α exp -γd i (4) m s,i ( Cq ) = 0 (5)
where γ is a constant enabling to control the decrease in belief in class C q with respect to the distance d to the neighbor x i .

Here, γ is set to the mean euclidean distance within the class C q . α is a constant enabling the doubt even if the extract to classify is placed at the exact same place as labelled data (here 0.95). m(C q ) represents the belief that is committed to C q , and m(C) is the belief committed to ignorance (any class is possible). m s,i ( Cq ) represents the belief committed to any class other than C q . The belief masses of neighbors x i of the same class C q are then aggregated in one unique belief mass m s q :

m s q ({C q }) = 1 - xi∈Cq 1 -α exp γd i (6)
m s q (C) = xi∈Cq 1 -α exp γd i (7)
Finally, all belief masses of all neighbors are aggregated in on final belief mass m s thanks to a Dempster combination (as shown in [START_REF] Denoeux | A k-nearest neighbor classification rule based on dempster-shafer theory[END_REF]):

m s ({C q }) = m s q ({C q }) r =q m s r (C) η (8)
m s (C) = M q=1 m s q (C) η ( 9
)
where η is a normalization constant:

η = M q=1   r =q m s r (C)   + (1 -M ) M q=1 m s q (10)
The value 1-η represents the conflict: the higher the value of η, the more the belief mass cannot be attributed to any class (ignorance included).

At the end of this process, a unique belief function is obtained, representing for each class the belief for the extract to belong to that class. A decision can be taken as to which label L is assigned to the considered extract. Several types of decision exist in the literature, here the attributed class is the class with the highest belief mass.

L = max i (m s (C i )), C i ⊂ C (11)
It also means that if m(C) is the maximum, the extract will be classified as unknown (doubt), which turns the ignorance into a class itself.

DATASET AND PARAMETER SELECTIONS

The accessibility to indoor sound data acquisition is still complicated and the datasets are often quite complex. In this paper, the signals composing the database are taken from different datasets:

• Speech: 27 extracts of 10 seconds of speech were extracted, mainly from the GTZAN database [START_REF] Tzanetakis | Musical genre classification of audio signals[END_REF] which regroups music and speech examples from various radio broadcasts and TV show, with different levels of background noise.

• Music: 30 extracts were also extracted from the GTZAN database.

• Impacts: To obtain sound of impacts of everyday life, we exploited the Sweet-home corpus created by Vacher et al. [START_REF] Vacher | The Sweet-Home speech and multimodal corpus for home automation interaction[END_REF]: in this corpus, several participants were put inside the automated house DOMUS and asked to accomplish daily routine during 2 hours such as cooking, manipulating objects, sleeping, etc..., which contains impact-like sounds. The impacts extracted include: doors and windows slamming, impact of objects on walls and table, etc.. 16 sounds were extracted in total from the datasets, all 10 seconds-long.

• Long sounds: This class is composed of a variety of sounds extracted partly from the Sweet-home corpus and partly from other sounds found on the internet. It is composed by sounds of footsteps, door knocking, dishes making etc.. The duration of those signals vary from 2 to 13 seconds. Each signal of the database is sampled at 22.5 kHz, and is processed with a 20 ms sliding window W with a 10 ms between two successive windows. For the experiments, the number of observed neighbors was set to 3, to enable us to decrease the size of the training set as much as possible.

RESULTS

A cross-validation method is applied to test the method on the dataset: training set and validation set are extracted randomly 1000 times from the dataset with a defined number of training samples. The separation in sets can be seen in table 1. A classification is considered an error if the class attributed to an extract is not the right one. The classification is not considered as an error if the system is unable to classify the extract.

The result of this classification can be seen in table 2 (line 1). The mean error (mean-number of misclassified signal) is 4.5 ( less than 10%). The mean number of signals classified as unknown is 4.1, which brings the total of non-correctly classified signals with our method to 8.6. It is to be noted that the misclassified signals are equally spread amongst the classes, which would indicate that the features do not favor a particular class. A good portion of the doubt (2 signals) belongs to the class "Long sounds" class, which makes sense as it is also the most complex class, while the rest of the doubt is divided almost equally amongst the other classes. Those results were compared to those given by a classic SVM with the same features (line 2 of table 2). The SVM gives out a higher number of hard misclassification, but a lower overall number of non-correctly classified extracts. The misclassified signals are higher in two classes: music and impact. It seems only logical that the added number of noncorrectly classified extracts is higher in the case of the belief-KNN as the SVM defines a separating hyperplane for the data, and hard classifies the data according to that hyperplane. The belief-KNN would tend to classify an extract which is close to labelled extracts of various classes as unknown (doubt class). The only class were there is a loss in classification compared with the SVM is the speech class. This difference cannot be easily explained, although it can easily be corrected by taking other modalities into account (especially vision).

CONCLUSION

The proposed classification method for indoor sounds signals obtains good classification performance, even though comparison with state-of-the art can be difficult. This method enables the non-classification of extracts in case the system does not reach a certain degree of confidence in one particular class, which makes it adapted to multi-sensor systems. Decreasing the semantic content of classes, making them more specifically adapted to the information conveyed by the sound modality enables the system to classify sounds that share a common fate .

Fig. 1 .

 1 Fig. 1. General framework of an ambient monitoring system: the proposed system is in the bold rectangle

Table 1 .

 1 Separation of each class into 2 different sets

	Class	Nb of training extracts Nb of test extracts
	Speech	11	16
	Music	11	19
	Impact	5	11
	Long Sounds	5	12
	Total	32	58

Table 2 .

 2 Results (mean number of misclassified signals)

		Speech Music Impact Long Doubt Total
	KNN	1.4	1.3	0.7	1.1	4.1	8.6
	SVM	0.6	2.5	2	1.1		6.3

ACKNOWLEDGMENTS

The authors would like to thank Vacher et al. [6] for the given access the Sweet-home corpus. This work has been partially supported by the LabEx PERSYVAL-Lab (ANR-11-LABX-0025).


LFW-4 1 SPECIALTY FIBERS Ultrathin optical fibers and surface acoustics make tiny sensors possible Brillouin light scattering driven by surface acoustic waves in tiny subwavelength-diameter optical fibers or microfibers can be used to create low-cost, novel optical sensors.

Subwavelength-diameter optical fibers-also known as optical microfibers, microwires, or nanowiresare the tiny cousins of standard optical fibers. 1,2 These hair-like slivers of glass, manufactured by tapering optical fibers down to a size 100 times smaller than a strand of hair, have a number of optical and mechanical properties that make them very attractive for both fundamental physics and technological applications.

In addition to providing strong light confinement and enhanced nonlinear optical effects, optical microwires also exhibit a large evanescent field, enabling applications not currently possible with comparatively bulky optical fibers. And although microfibers can greatly enhance the optical Kerr effect and stimulated Raman scattering for supercontinuum generation, Brillouin scattering in these tiny waveguides has yet to be explored. 3 By confining light in silica optical microfibers 50 times thinner than a human hair, our group at the CNRS Femto-ST Institute in Besançon, France, in collaboration with colleagues at the Institut d'Optique in Paris, has observed for the first time a new type of Brillouin light scattering from surface acoustic waves. This phenomenon, which varies according to the fiber's environment, can be used to develop low-cost optical diffusers and innovative, highly sensitive optical sensors that target new applications.

Scattering light from sound

Brillouin scattering is one of the most important nonlinear optical processes in optical fibers and results from the coherent interaction between light and acoustic waves. It has important applications ranging from optical sensors to telecommunications and also enables slow and fast light propagation. In standard optical fibers, light excites and interacts with shear and longitudinal bulk elastic waves, giving rise to well-known physical effects such as forward-guided acoustic wave Brillouin scattering (GAWBS) and backward-stimulated Brillouin scattering (SBS). 4,5 In a subwavelength-diameter optical fiber, we recently reported the first experimental observation of Brillouin light scattering from surface acoustic waves. 6 To observe these waves, we injected a laser beam into a 1 µm diameter silica fiber wire. As the beam travels down the fiber, it induces infinitesimal vibrations in the wire through the electrostrictive optical force, displacing the surface by a few nanometers.

LFW-4 2

The distortions lead to surface acoustic waves that travel along the wire surface at 3400 m/s. These acoustic phonons in turn affect the light propagation, that is, a part of the light is backscattered with a slight shift of its carrier frequency due to the Doppler effect. This frequency shift is on the order of 5-6 GHz, far from the standard Brillouin frequency shift of standard optical fibers (about 11 GHz).

The silica microwires used in our experiment were fabricated by our Institut d'Optique colleagues by heating and stretching standard single-mode optical fibers (measuring 125 µm in diameter) down to a diameter of 1 µm over a length of 8 cm (see Fig. 2). In our lab at the Femto-ST Institute, we configured the experimental setup to detect the acoustic waves.

Our pump source was a narrow-linewidth continuous-wave distributed feedback (DFB) laser operating at 1550 nm. The laser output was split into two beams using a fiber coupler. One beam was amplified and injected in the optical microwire through an optical circulator, while the other beam was used for detection via a heterodyne technique in which the backscattered light from the microwire was mixed with the input coming from a second fiber coupler. The resulting beat note was then detected using a fast photodiode and its Brillouin spectrum was recorded using an electrical spectrum analyzer.

For an input power of 100 mW, the resulting experimental spectrum displays the clear emergence of several frequency peaks with different weights in a radio-frequency range from 6 to 11 GHz. First, the high frequency at 10.86 GHz originates from standard Brillouin scattering in the untapered fiber sections. More importantly, two other peaks appear around 6 GHz that result from Brillouin scattering from surface waves, whereas the three other resonances around 9 GHz are clearly the signature of hybrid shear and longitudinal acoustic waves.

Simulations

Our observations were subsequently confirmed through numerical simulation using the equation of elastodynamics including the optical force, which shows the computed sound density generated by light in the silica microwire as a function of frequency and wire diameter (see Fig. 2). 6 As shown in the simulation, we experimentally retrieve most of the surface and hybrid acoustic resonances around 6 and 9 GHz.

The simulations and experiments also confirm that, for a given microwire diameter, multiple hybrid waves with widely spaced frequencies can be simultaneously excited. This means that in such small subwavelength waveguides, the light-sound interaction is fundamentally different from standard optical fibers. In other words, the Brillouin spectrum is not simply the signature of a single bulk longitudinal sound wave.

Instead, in such tiny waveguides, waveguide boundary conditions induce a strong coupling of shear and longitudinal displacements, resulting in much richer dynamics of light interaction with sound. In fact, the hybrid waves are not orthogonal and thus strongly interact with associated transverse and axial displacements in the microwire for four of the surface and hybrid modes. As an example, a surface LFW-4 3 acoustic mode at 5.382 GHz exhibits a transversal displacement of a few picometers and a weak axial displacement just below the wire surface. Clearly, this is the signature of a surface Rayleigh wave that combines both a longitudinal and transverse motion to create mechanical ripples with an elliptic orbit motion (see Fig. 3).

Sensing applications

In standard optical fibers, light travels essentially within the 10 µm diameter core of the fiber and thus does not generate surface waves. But when light is confined in a subwavelength-diameter waveguide, our research confirms the first reported observation of surface acoustic wave Brillouin scattering (SAWBS). This SAWBS effect is significant in that it may foster the investigation of such surface waves in many different photonic platforms, including photonic crystal fibers and integrated silicon or chalcogenide photonic chips.

Since the waves generated by the confinement of the light travel along the surface, they are sensitive to environmental factors such as temperature, pressure, and ambient gas. This makes it possible to design highly sensitive and compact optical sensors for industrial and scientific applications.

Our investigations also help improve knowledge of the fundamental interaction between light and sound on an infinitesimally small scale. Surface Brillouin scattering also opens new opportunities in other domains such as nonlinear plasmonics. For instance, one could expect a strong interaction between surface waves and surface plasmons by depositing a thin layer of gold or silver on the microwire surface.

FIGURE 1 . 4 4

 14 FIGURE 1. A schematic shows the tapered silica microwire (a) used in the Brillouin experimental setup (b). DFB is the distributed feedback laser, FC is the 10/90 fiber coupler, and EDFA is an erbium-doped

FIGURE 2 .FIGURE 3 .

 23 FIGURE 2. Numerical simulations are shown of the acoustic wave spectrum and displacements in silica microwire. The top shows a color plot of sound density as a function of frequency for a wire diameter varying from 0.6 to 3.5 µm; white arrows indicate the surface and hybrid acoustic waves and the anticrossing points due to the strong coupling action. The bottom shows transverse and axial displacements in the microwire associated with surface and hybrid acoustic modes labeled A, B, C, and D.