Wikipedia provides a knowledge base for computing word relatedness in a more structured fashion than a search engine and with more coverage than WordNet. In this work we present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet when applied to the largest available dataset designed for that purpose. The best results on this dataset are obtained by integrating Google, WordNet and Wikipedia based measures. We also show that including Wikipedia improves the performance of an NLP application processing naturally occurring texts.

In this paper we present an extension of a machine learning based coreference resolution system which uses features induced from different semantic knowledge sources. These features represent knowledge mined from WordNet and Wikipedia, as well as information about semantic role labels. We show that semantic features indeed improve the performance on different referring expression types such as pronouns and common nouns. 1

Wikipedia provides a semantic network for computing semantic relatedness in a more structured fashion than a search engine and with more coverage than WordNet. We present experiments on using Wikipedia for computing semantic relatedness and compare it to WordNet on various benchmarking datasets. Existing relatedness measures perform better using Wikipedia than a baseline given by Google counts, and we show that Wikipedia outperforms WordNet on some datasets. We also address the question whether and how Wikipedia can be integrated into NLP applications as a knowledge base. Including Wikipedia improves the performance of a machine learning based coreference resolution system, indicating that it represents a valuable resource for NLP applications. Finally, we show that our method can be easily used for languages other than English by computing semantic relatedness for a German dataset.

Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods.

Abstract. Semantic Similarity relates to computing the similarity between conceptually similar but not nec-essarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic sim-ilarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capa-ble for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising perfor-mance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-the-art semantic similarity retrieval methods utilizing ontologies.

Abstract. Web service technology allows access to advertised services despite of their location and implementation platform. However, considerable differences on structural, semantical and technical levels along with the growing number of available web services makes their discovery a significant challenge. Keyword-based matchmaking methods can help users to locate quickly the set of potentially useful services, but they are insufficient for automatic retrieval. On the other hand, the high cost of formal ontology-based methods alienates service designers from their use in practice. Several information retrieval approaches to assess the similarity of web services have been proposed. In this paper we proceed with such a study. In particular, we examine advantages of using Vector-Space Model, WordNet and semantic similarity metrics for this purpose. A matching algorithm relying on the above techniques is presented and an experimental study to choose the most effective approach is provided. 1

Improving web service discovery constitutes a vital step for making the Service Oriented Computing (SOC) vision of dynamic service selection, composition and deployment, a reality. Matching allows for comparing service requests of users with descriptions of available service implementations, and sits at the heart of the service discovery process. During the last years, several matching algorithms for comparing user requests to service interfaces have been suggested, but unfortunately there are no consistent comparative experimental evaluations of existing service discovery methods. This paper firstly reviews several state-ofthe-art approaches to matching using syntactic, semantic and structural information from service interface descriptions. Secondly, it evaluates the efficacy of several key similarity metrics that underpin these approaches, using a uniform corpus of web services. Thirdly, this paper develops and experiments with a novel style of matching that allows for blending various existing matching approaches and makes them configurable to cater service discovery given domain-specific constraints and requirements.

In this work we have used five semantic similarity measures and WordNet to add information to an ontology, the Common Procurement Vocabulary. The added information is used to automatically classify product descriptions according to the Common Procurement Vocabulary. It is shown that the similarity measure proposed by Leacock and Chodorow is the most suitable for this task, out of the five measures compared. Leacock-Chodorow shows average precision between 0.684 and 0.711 and recall between 0.845 and 0.977, depending on whether threshold is used or not. Baseline average precision peaks at 0.592. 1

Semantic similarity between words or phrases is frequently used to find matching correlations between search queries and documents when straightforward matching of terms fails. This is particularly important for searching in visual databases, where pictures or video clips have been automatically tagged with a small set of semantic concepts based on analysis and classification of the visual content. Here, the textual description of documents is very limited, and semantic similarity based on WordNet’s cognitive synonym structure, along with information content derived from term frequencies, can help to bridge the gap between an arbitrary textual query and a limited vocabulary of visual concepts. This approach, termed concept-based retrieval, has received significant attention over the last few years, and its success is highly dependent on the quality of the similarity

Abstract. Some of today’s most widely spread applications are social systems where people can form communities and share knowledge. However, knowledge sharing is not always effective and communities often do not sustain. Can user modelling approaches help to identify what support could be offered and how this would benefit the community? The paper presents algorithms for extracting a model of a closely-knit virtual community following processes identified as important for effective communities. The algorithms are applied to get an inside of a real virtual community and to identify what support may be needed to help the community function better as an entity.