Improving Search and Decision-Making with Semantics
We’ve all heard about how Google’s proverbially simple search form has led professionals to expect similar simplicity from search solutions provided by corporate IT. Except this model doesn’t really work, and it’s costing millions of dollars every year in time wasted when professionals don’t find, and have to re-create, key information.
The reason it doesn’t work is that while every organization has a specific worldview, search engines are essentially flying blind. By worldview, I mean the inventory of business objects the organization cares about (products, geographies, customers, processes, etc.) and their relationships, that are typically captured in a taxonomy or ontology. While professionals implicitly want to search for information according to their worldview, search engines don’t offer them a practical way to do so.
But the very need for search suggests a deeper problem: it’s actually our entire information systems that are flying blind. “Wouldn’t there be a way,” we might wonder, “for our systems to understand all this content, and to suggest some conclusions–even draft conclusions–so we could spend less time searching, and instead, invest the bulk of it analyzing and making decisions?”
Semantics Provides Meaning
The missing piece in this puzzle is a “meaning engine” that would understand unstructured content through the lens of your organization’s worldview. It exists: it’s called a semantic enrichment platform.
A semantic enrichment platform ingests your organization’s taxonomy or ontology and applies it to your content at scale. Leveraging natural language processing, it understands your content the same way humans do. It recognizes topics that are relevant to your business, entities of interest, their attributes and relationships, and converts them into structured data, that can be used standalone, or as metadata describing your content deeply and consistently. In energy, for example, entities of interest might include commodities, trading companies, and the countries where they do business.
Better Metadata Accelerates Search
When used as metadata, this data acts as an eye-opener for search engines that can finally see your content through your own worldview. This redefines the search experience by offering end-users new tools to locate what they are looking for:
- Faceted Navigation enables end-users to search by business entity or topic (for example by company name, commodity type or region), helping to find the most relevant content in just a few clicks.
- Links to relevant information provide convenient access to structured information about entities of interest so users don’t have to collate it themselves. For example, each company name could be linked to data about its activities.Topic Pages concentrate all information about a specific topic in one convenient access point so users don’t need to sift through all other materials to access it. A topic page on electricity would, for example, filter out information related to other energy sources.
- Content Recommendation uses metadata to surface other documents with similar topics, promoting serendipitous discovery of relevant information. A document on a merger in the Gas sector might point to reports of other, similar operations.
Such mechanisms significantly accelerate and simplify search tasks, offering not only time and cost savings, but also more informed decision-making.
Better Data Improves Decision-Making
But semantically-extracted information can be used for its informative,rather than descriptive, value. Not as metadata, but as standalone data. This opens the door to applications that address the above blindness at a deeper level, providing higher-level and faster insight into the subject matter at hand.
One of semantics’ capabilities is to recognize not only entities, but also their relationships (often expressed as triples). One such relationship might for example indicate that company A is a “supplier of” company B. Information value from these relationships may come into play under a variety of scenarios.
- Knowledge Bases (or Graphs) integrate such structured information at scale so they can then be queried. One might contain, for a given commodity, links to all suppliers.
- Complex Reasoning can be performed on these knowledge bases, enabling business applications to provide higher degrees of automation in decision-making tasks, for example, automatically balancing supply by identifying alternative suppliers when one announces production issues.
- Analytics & Visualizations provide dashboards that sit “on top of” the data and reveal its meaning on a more holistic level. For example, a network graph could plot all company relationships in natural gas, indicating which companies might be exposed to increasing prices in a given region.
Lastly, semantics can also be exploited to deliver Question Answering Systems that offerend-users a way to get answers to questions formulated in natural language (“Which electricity providers have the most diversified supply chain?”) instead of engaging in search.
Semantics Provides Faster Insights and Better Decisions
As can be seen from the examples above, semantics is the “meaning engine” that ensures end-users can overcome search’s blindness and access information through the specific worldview relevant to their work. But this engine brings meaning to more than your search engine: it’s your information management as a whole that benefits, bringing the promise of smarter applications that efficiently handle more of the groundwork, accelerate time-to-insight and support better decisions.
Expert System is a leading provider of cognitive computing and text analytics software based on the proprietary semantic technology of Cogito. The products and solutions based on Cogito’s advanced analysis engine and complete semantic network exceed the limits of conventional keywords, and offer a complete set of features including: semantic search, text analytics, development and management of taxonomies and ontologies, automatic categorization, extraction of data and metadata, and natural language processing.