Bridging the Search Gap between the Web of Pages and Web of Data by Combining Ontological Document Expansion with Text Search

The Semantic Web extends traditional web documents, i.e. the Web of Pages, with conceptual structures based on ontologies and metadata, i.e. the Web of Data. This paper presents a hybrid document search approac h c mbining the benefits of the traditional text search of literal documents and the sema ntic search based on their underlying conceptual structures. The approa ch is based on document expansion, where documents are automatically annotated w ith not only the concepts explicitly present in a given document, but also with the on tologically related concepts using smaller weights. Our test results using the CL EF Test Suite suggest that document expansion alone achieves better rec all than text search at the expense of precision. As a solution, a method of combining document expansion with text search is presented in which better recall was ob t ined without sacrificing precision. This approach seems promising when integ ratin unstructured, textual content with the Semantic Web of Data. 1 Text Search vs. Semantic Search The Semantic Web 1 extends web pages, documents, and other web materials with m achine understandable, ontology-based [21] network of meta data attached to the contents. In the case of text documents, a central part of the met adata describes the subject matter of the text, and is directly related with the literal w ords and expressions of the documents. When searching text documents, two major approac hes are available: 1. In text search the query is matched against the textual expressions of the d ocuments, and search takes place in a literal space. In spite of the succ e s of traditional text search engines, this approach has severe fundamental limit ations [5] in its basic form. For example, recall is lowered because the query word c annot be matched with synonyms or semantically close terms. For example, que ry “student” does not match documents about pupils, “bird” does not match descrip tions about eagles, and query “nordic country” does not match with Finland or Swe den. At the same time, precision is lessened due to polysemy and homonymy of w ords. For example, query “bank” matches financial banks, blood banks, and river banks. It is often possible to improve precision at the expense of recall, and v ice versa [4]. 1 http://www.w3. org/2001/sw/ 2. Semantic search [8] tries to address the limitations of text search by perfor ming search in a conceptual space, based on disambiguated concep ts rather than literal words, and by utilizing semantic networks of concepts under lying the texts. Such a search should be more precise since homonymous queries can be disambiguated before the query, or by clustering the results according to d ifferent interpretations of the query afterwards. At the same time, recall can be impro ved by extending the query to synonyms and semantically related ontological con cepts by query expansion [15, 11]. Both of these methods can be further refined in the WWW environme nt through the use of links between pages to find relevant documents not incl uded in the original result set and to rank the results to better reflect their authoritat iveness [16]. Automated query expansion methods can be broken down into 1) methods based on search results and 2) ones based on knowledge structures, the latter of which can be further grouped into collection dependent and collectio n independent methods [4]. Methods based on search results first perform a query using th e query terms as given by the user after which a new query is formed based on terms with h igh occurrence in the result set. Methods based on knowledge structures either us e corpus-based knowledge of, for example, correlations between different terms or us e some a priori knowledge like relations between different concepts. This latter app roach lends itself well todocument expansion where the query expansion is not done dynamically in respons e to a user query but rather in advance during indexing. Unfortunately semantic search is not a panacea but has its ow n difficulties and limitations. For example, expanding the query or documents sem antically raises recall but may dramatically lower the precision unless the expansion s trategy is carefully tuned [11]. On the other hand, matching precise search concepts wi th conceptual metadata may lower recall because conceptual representations canno t model very well e.g. the uncertain or fuzzy meaning of real world concepts [9]. Furth e more, the research tradition of information retrieval [1] has produced lots of use ful methods and techniques such as TF-IDF [19] for ordering search results according to their relevance w.r. t. the query. Oddly enough, the issue of relevance has not yet been d iscussed much in the semantic search community, although its has been a key issue behind the success of search systems such as Google. It therefore seems worthwhile to investigate whether it is u sef l to combine ideas from text search with those of semantic search, as suggested . g. in [10] for an optimal hybrid search strategy. This paper presents such a study in t he application domain of news paper articles. In the following, we present a document expansion method utilizing ontologies by which the text documents can be annotated auto matically and different semantic search strategies can be performed. We then test so me combinations of semantic search and text search and measure the results based o n an article data set of the CLEF Test Suite 2 and its golden standard. The results of our experiments sugg est that substantial benefits in terms of precision, recall, and relevance can be obtained by combining methods of text and semantic search in smart ways. 2 http://www.clef-campaign.org/ 2 A Hybrid Search and Recommendation Architecture 2.1 Document expansion versus query expansion in the Semant ic Web domain The difference between document expansion and query expans ion is basically the timing of the expansion step. In document expansion the terms ar e expanded during the indexing phase for each individual document. In query expan sion only the query terms are expanded and this is done dynamically when a user perform s a search into the database. Document expansion, though less frequently used than query expansion, has some important benefits when used in the Semantic Web domain. Firs t of all it does not put a strain on the search system computationally as it is perfor med in advance during the indexing stage. Also, when done using ontologies, it expand s the documents or other resources to the Semantic Web of Data allowing these documen ts and resources to be linked together in new ways. If the ontological expansion is done in the query phase, it ex pands only the query and not the actual documents and resources. This means that i n order to link resources through more complex relations the links are made longer whi ch, in the case of larger ontologies, can expand the query originally comprised of a f ew terms into thousands of terms. An example of this would be if the user searches a datab ase for all documents that are about birds. Using a bird name ontology like AVIO 3 with 9740 individual species would find all the documents mentioning any individual bird s pecies even if they lack the original query term, but the query itself would be almost ten thousand terms long. Most search engines have limits to the size of the input which renders queries of thousands of terms impossible. 2.2 Ontological Concept Clustering The hybrid search architecture and process used in our case s tudy, based on text search and semantic search using ontology based document expansio n, is depicted in Figure 1. The process starts with the lemmatization of a given documen t (cf. the upper left corner of the figure). After this, stop words are filtered out and t he text is indexed into a conventional term-document matrix [20]. In order to facilitate semantic search, each lemmatized ter m in a document is matched with ontological concepts using labels present in an ontolo gy (cf. box “concept matching” in the figure). If a match is found, then the concept’s URI is added to the document’s metadata as a subject annotation. Several ontologie s can be used during indexing and the concepts found from each ontology are saved in their o wn subject fields. The characteristics required of an ontology are simply the exis t nce of concept labels and some sort of relations between concepts, so in theory thesau ri can also be used, though the typically more limited relations would result in worse p rformance overall. A separate index is built for each ontology/field. The process is f ully automatic since we are dealing with thousands of news paper articles, which makes m anual checking infeasible. Homonymous terms are not disambiguated by human interv ention or using other techniques semantically, but are indexed using multiple me anings. 3 http://www.yso. fi/onki/avio/? l=en Fig. 1.The process of document expansion through ontological concept clu stering The document expansion is performed in the box “concept clus ering” by expanding each matched concept c into a larger set that consists of other concepts semantical ly related toc in the ontology. The goal of using document expansion is to pr ovide the user with larger, relevant result sets by using the semantic information underlying the documents. Document expansion is done by following an ontol gy specific pattern expressed in a pattern language developed for the task. A patte rn is comprised of paths made up of the relations in the target ontology. Each path spe cifies the relations, or steps, that make up the path, the depth to which those relatio ns are to be followed, as well as a weighting coefficient which determines the importa nce of related concepts found using the path. The idea of giving a weight to related co ncepts is an extension to earlier query expansion patterns such as [11]. For exampl e, the direct superclasses of c can be given a high wei