Semantic Documents Relatedness using Concept Graph Representation

Ni, Yuan; Xu, Qiongkai; Cao, Feng; Mass, Yosi; Sheinwald, Dafna; Zhu, Hui; Cao, Shao Sheng

doi:10.1145/2835776.2835801

Cited by 44 publications

(40 citation statements)

References 12 publications

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“…This novel representation has recently allowed researchers to design new algorithms that significantly boost the performance of known approaches in several IR applications, such as query understanding, documents clustering and classification, text mining, etc. [15,17,36,39,43,52].…”

Section: Related Workmentioning

confidence: 99%

“…Word embeddings [30] is a very recent Natural Language Processing (NLP) technique that aims at mapping words or phrases to low dimensional numerical vectors that are faster to manipulate and offer interesting distributional properties to compare and retrieve "similar" words or phrases [30]. This latent representation has been recently extended [37,38] to learn two different forms of representations of Wikipedia entities [39]: (1) ENTITY2VEC [37] learns the latent representation of entities by working at textuallevel over the content of Wikipedia pages, and (2) DEEPWALK [38] learns the latent representation of entities by working on the hyper-link structure of the Wikipedia graph via the execution of random walks that start from a focus node (i.e. the entity to be embedded) and visit other nearby nodes (that provide its contextual knowledge).…”

Section: Related Workmentioning

confidence: 99%

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Wiser: A semantic approach for expert finding in academia based on entity linking

Cifariello

Ferragina

Ponza

2019

Information Systems

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We present WISER, a new semantic search engine for expert finding in academia. Our system is unsupervised and it jointly combines classical language modeling techniques, based on text evidences, with the Wikipedia Knowledge Graph, via entity linking.WISER indexes each academic author through a novel profiling technique which models her expertise with a small, labeled and weighted graph drawn from Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the author's publications, whereas the weighted edges express the semantic relatedness among these entities computed via textual and graph-based relatedness functions. Every node is also labeled with a relevance score which models the pertinence of the corresponding entity to author's expertise, and is computed by means of a proper random-walk calculation over that graph; and with a latent vector representation which is learned via entity and other kinds of structural embeddings derived from Wikipedia.At query time, experts are retrieved by combining classic document-centric approaches, which exploit the occurrences of query terms in the author's documents, with a novel set of profile-centric scoring strategies, which compute the semantic relatedness between the author's expertise and the query topic via the above graph-based profiles.The effectiveness of our system is established over a largescale experimental test on a standard dataset for this task. We show that WISER achieves better performance than all the other competitors, thus proving the effectiveness of modelling author's profile via our "semantic" graph of entities. Finally, we comment on the use of WISER for indexing and profiling the whole research community within the University of Pisa, and its application to technology transfer in our University.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Wiser: A semantic approach for expert finding in academia based on entity linking

Cifariello

Ferragina

Ponza

2019

Information Systems

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“…E2V utilizes textual information to capture latent word relationships. Similar to Zhao et al (2015); Ni et al (2016), we use Wikipedia articles as training corpus to learn word vectors and reserved hyperlinks between entities.…”

Section: Models For Comparisonmentioning

confidence: 99%

A Trio Neural Model for Dynamic Entity Relatedness Ranking

Nguyen¹,

Nejdl²

2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

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Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in static settings and an unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity-relations are very dynamic over time.In this work, we propose a neural networkbased approach for dynamic entity relatedness, leveraging the collective attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.

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“…In contrast to the approaches outlined above which assigns weights to concepts of an ontology, the approach presented by Ni et al [107] concerns with assigning weights to concepts acquired from the knowledge base resource known as DBpedia [13]. The approach employs two assignment methods to compute and assign weights to a concept: a global method and a local method.…”

Section: Weighting Scheme -Concept Importancementioning

confidence: 99%

A Hybrid Concept Learning Approach to Ontology Enrichment

Kastrati

Imran

Yayilgan

2018

Innovations, Developments, and Applications of Semantic Web and Information Systems

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Declaration of AuthorshipI, Zenun Kastrati, hereby declare that this thesis and the work presented in it is entirely my own. Where I have consulted the work of others, this is always clearly stated.Signed:(Zenun Kastrati) Date: SummaryWe are living in the age of internet where massive amount of information is produced from various digital resources on daily basis. The information of these resources is typically stored in unstructured textual format such as reports, news, e-mails, blogs, etc., therefore, a proper classification and organization of this huge amount of information is apparently needed. In this regard, an automatic classification, particularly ontology-based classification, plays an important role in helping people to classify and organize the information accordingly. The ontology-based classification system is an automatic system that utilizes the ontology in order to take advantages of organizing and classifying the knowledge in a more structural and formal way, thus providing better classification accuracy comparing to the traditional keyword-based classification system.The performance of an ontology-based document classification system can be affected by several aspects involved in the entire classification process that generally is constituted of steps such as document collection and preprocessing, document representation, dimensionality reduction, and the classifier. It is almost impossible to address all these research aspects in order to obtain performance improvement in a single dissertation research work, therefore we selected to work on the aspects that we consider are either rarely studied or have a crucial role on the ontology-based classification system. Document representation is one of the main aspects that affects the performance of ontology-based document classification, thus the first research aspect that we investigated is enriching document representation with semantics utilizing the background knowledge exploited by ontologies. The background knowledge derived from an ontology is embedded in a document using a matching technique. The idea behind this technique is mapping of terms that occur in a document with the relevant ontology concepts by searching only the presence of concepts labels in that document. Searching only the presence of concepts labels occurring in a document limits the capabilities of the classification system to capture and exploit the entire conceptualization involved in that document due to the semantic gap issue, the lack of an in depth-coverage of concepts, and the ambiguity problem. In this thesis, the focus is placed on the conceptual document representation, in which, a document is associated with a set of concepts not only by looking for the appearance of concept labels, but also through the acquisition of lexical information integrated (linked) to the ontology to enriching its coverage with new concepts. In this respect, an automatic ontology concept enrichment model is developed to enrich ontologies with new concepts in order to provide a broader co...

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Semantic Documents Relatedness using Concept Graph Representation

Cited by 44 publications

References 12 publications

Wiser: A semantic approach for expert finding in academia based on entity linking

Wiser: A semantic approach for expert finding in academia based on entity linking

A Trio Neural Model for Dynamic Entity Relatedness Ranking

A Hybrid Concept Learning Approach to Ontology Enrichment

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