Personal ontologies: Generation of user profiles based on the YAGO ontology

Calegari, Silvia; Pasi, Gabriella

doi:10.1016/j.ipm.2012.07.010

Cited by 48 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Categories can be identified explicitly by users [29], or they can be inferred based on the user past activity through a categorization method [30]. Some approaches attempt to build specific ontologies to represent user interests [31]. Others use ontologies to conceptualize different aspects of the context, such as the themes of the user's information need and the specific data the user is looking for to achieve the task that has motivated the search.…”

Section: Context Extraction and Modelingmentioning

confidence: 99%

Searching in the Context of a Task: A Review of Methods and Tools

Maguitman¹

2018

CLEIej

View full text Add to dashboard Cite

Contextual information extracted from the user task can help to better target retrieval to task-relevant content. In particular, topical context can be exploited to identify the subject of the information needs, contributing to reduce the information overload problem. A great number of methods exist to extract raw context data and contextual interaction patterns from the user task and to model this information using higher-level representations. Context can then be used as a source for automatic query generation, or as a means to refine or disambiguate user-generated queries. It can also be used to filter and rank results as well as to select domain-specific search engines with better capabilities to satisfy specific information requests. This article reviews methods that have been applied to deal with the problem of reflecting the current and long-term interests of a user in the search process. It discusses major difficulties encountered in the research area of context-based information retrieval and presents an overview of tools proposed since the mid-nineties to deal with the problem of context-based search.

show abstract

Section: Context Extraction and Modelingmentioning

confidence: 99%

Searching in the Context of a Task: A Review of Methods and Tools

Maguitman¹

2018

CLEIej

View full text Add to dashboard Cite

show abstract

“…ConceptNet [118] User Profile Creation for Personalized Web Search ODP [41], YAGO [36] Cross-Lingual Information Retrieval Wikipedia [1]. Of all the knowledge bases, Wikipedia has, so far, proven to be one of the most valuable resources; in fact knowledge bases such as DBPedia [23] and YAGO [195] have been derived from Wikipedia.…”

Section: Problem Knowledge Basementioning

confidence: 99%

Utilising Wikipedia for Text Mining Applications

Qureshi

2016

SIGIR Forum

View full text Add to dashboard Cite

The process whereby inferences are made from textual data is broadly referred to as text mining. In order to ensure the quality and effectiveness of the derived inferences, several approaches have been proposed for different text mining applications. Among these applications, classifying a piece of text into pre-defined classes through the utilisation of training data falls into supervised approaches while arranging related documents or terms into clusters falls into unsupervised approaches. In both these approaches, processing is undertaken at the level of documents to make sense of text within those documents. Recent research efforts have begun exploring the role of knowledge bases in solving the various problems that arise in the domain of text mining. Of all the knowledge bases, Wikipedia on account of being one of the largest human-curated, online encyclopaedia has proven to be one of the most valuable resources in dealing with various problems in the domain of text mining. However, previous Wikipedia-based research efforts have not taken both Wikipedia categories and Wikipedia articles together as a source of information. This thesis serves as a first step in eliminating this gap and throughout the contributions made in this thesis, we have shown the effectiveness of Wikipedia category-article structure for various text mining tasks. Wikipedia categories are organized in a taxonomical manner serving as semantic tags for Wikipedia articles and this provides a strong abstraction and expressive mode of knowledge representation. In this thesis, we explore the effectiveness of this mode of Wikipedia's expression (i.e., the category-article structure) via its application in the domains of text classification, subjectivity analysis (via a notion of "perspective" in news search), and keyword extraction. First, we show the effectiveness of exploiting Wikipedia for two classification tasks i.e., 1- classifying the tweets1 being relevant/irrelevant to an entity or brand, 2- classifying the tweets into different topical dimensions such as tweets related with workplace, innovation, etc. To do so, we define the notion of relatedness between the text in tweet and the information embedded within the Wikipedia category-article structure. Then, we present an application in the area of news search by using the same notion of relatedness to show more information related to each search result highlighting the amount perspective or subjective bias in each returned result towards a certain opinion, topical drift, etc. Finally, we present a keyword extraction strategy using community detection over the Wikipedia categories to discover related keywords arranged in different communities. The relationship between Wikipedia categories and articles is explored via a textual phrase matching framework whereby the starting point is textual phrases that match Wikipedia articles' titles/redirects. The Wikipedia articles for which a match occurs are then utilised by extraction of their associated categories, and these Wikipedia categories are used to derive various structural measures such as those relating to taxonomical depth and Wikipedia articles they contain. These measures are utilised in our proposed text classification, subjectivity analysis, and keyword extraction framework and the performance is analysed via extensive experimental evaluations. These experimental evaluations undertake comparisons with standard text mining approaches in the literature and our Wikipedia framework based on its category-article structure outperforms the standard text mining techniques.

show abstract

“…Traditional approaches for user profiling often rely on sets of keywords for the summary of user preferences [2]. This representation scores with its simplicity and thus the possibility to be efficiently computed and processed.…”

Section: User Profilingmentioning

confidence: 99%

“…However, recent approaches go a step further by including the semantic relations that are identified between the concepts of the ontology into the profile -and thus, extending it from a set of keywords to a profile ontology. This may be done using further background knowledge, or just using the sequence of user actions to interconnect topic terms [2]. The first type of methods largely relies on data mining and fuzzy relational algebra -the co-occurrence pattern of terms is used to predict a relation between them for the current user's context [23,9].…”

Section: User Profiling Using Ontologiesmentioning

confidence: 99%

Automatic ontology-based user profile learning from heterogeneous web resources in a big data context

2013

View full text Add to dashboard Cite

The Web has developed to the biggest source of information and entertainment in the world. By its size, its adaptability and flexibility, it challenged our current paradigms on information sharing in several areas. By offering everybody the opportunity to release own contents in a fast and cheap way, the Web already led to a revolution of the traditional publishing world and just now, it commences to change the perspective on advertisements. With the possibility to adapt the contents displayed on a page dynamically based on the viewer's context, campaigns launched to target rough customer groups will become an element of the past. However, this new ecosystem, that relates advertisements with the user, heavily relies on the quality of the underlying user profile. This profile has to be able to model any combination of user characteristics, the relations between its composing elements and the uncertainty that stems from the automated processing of real-world data. The work at hand describes the beginnings of a PhD project that aims to tackle those issues using a combination of data analysis, ontology engineering and processing of big data resources provided by an industrial partner. The final goal is to automatically construct and populate a profile ontology for each user identified by the system. This allows to associate these users to high-value audience segments in order to drive digital marketing.

show abstract

Personal ontologies: Generation of user profiles based on the YAGO ontology

Cited by 48 publications

References 28 publications

Searching in the Context of a Task: A Review of Methods and Tools

Searching in the Context of a Task: A Review of Methods and Tools

Utilising Wikipedia for Text Mining Applications

Automatic ontology-based user profile learning from heterogeneous web resources in a big data context

Contact Info

Product

Resources

About